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Ecological Engineering
journal homepage: www.elsevier.com/locate/ecoleng
Long-term forest succession improves plant diversity and soil quality but notsignificantly increase soil microbial diversity: Evidence from the LoessPlateauYulin Liua, Guangyu Zhua, Xuying Haia, Jiwei Lib, Zhouping Shangguana,b, Changhui Penga,c,Lei Denga,b,⁎
a State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling, Shaanxi 712100, Chinab Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling, Shaanxi 712100, Chinac Center of CEF/ESCER, Department of Biological Science, University of Quebec at Montreal, Montreal H3C 3P8, Canada
A R T I C L E I N F O
Keywords:Forest successionMicrobial diversityPlant diversitySoil carbonSoil nitrogenVegetation restoration
A B S T R A C T
Many studies have focused on the processes of vegetation succession, however, the dynamics of soil microbesand the synergy between vegetation and soil are still poorly understood following vegetation succession. Thisstudy focused on a forest succession sequence including farmland, grassland, shrubland (i.e., Hippophae rham-noides), pioneer forest (i.e., Populus davidiana), and climax forest (i.e., Quercus liaotungensis) on the Loess Plateauof China, to explore plant and soil changes, as well as soil microbial community dynamics. The results showedthat litter biomass, soil organic carbon (SOC), total nitrogen (TN) and the ratio of SOC to TN exhibited anincreasing trend in the whole process of the forest succession, and NH4
+, microbial biomass carbon (MBC),microbial biomass nitrogen (MBN), and the ratio of dissolved organic carbon (DOC) to dissolved organic ni-trogen (DON) had significantly increased before the shrubland stage, and then they were going to be stable.During the forest succession, the main bacterial phyla present were Proteobacteria, Actinobacteria, andAcidobacteria, and the predominant fungal phyla were Ascomycota and Basidiomycota. The soil microbial com-munity composition was stable and did not change significantly, but the bacteria and fungal communities wereassociated with specific plant or soil properties. It was proved that the change of soil microbial community wasclosely related to vegetation and soil community changes. The results suggested that long-term forest successionnot only improves plant diversity, but also improves soil biology and quality, even though it does not sig-nificantly increase soil microbial diversity. The findings enhance the understanding of the impact of soil mi-crobial ecological characteristics and provide an important guidance for the sustainable management of forestecosystems following long-term natural vegetation restoration.
1. Introduction
Vegetation succession refers to the process of restoring plant andsoil communities (Walker et al., 2007; Herrera Paredes et al., 2016;Zhao et al., 2019), and is one of the strategies to control land de-gradation (Liu et al., 2008; Deng et al., 2018a). The development ofvegetation communities can effectively reduce soil loss, increase bio-diversity and improve relevant ecological functions (Bullock et al.,2011; Deng et al., 2018b). Studies have confirmed that vegetation candirectly or indirectly change soil properties and maintain soil fertilitydue to the close relationship between plants and microorganisms (Wanget al., 2011; Deng et al., 2018a), thereby affecting soil microbial com-munity and structure, activity and function (Schlatter et al., 2015;
Frouz et al., 2016; Wang et al., 2019b). However, the succession ofvegetation is a long-term process, and the effects of vegetation char-acteristics at different stages have different effects on soil microbialdiversity (Liu et al., 2010; Von Gillhaussen et al., 2014; Wilsey et al.,2015; Wubs et al., 2016), which may change depending on local climateand soil conditions (Li et al., 2011; Munroe et al., 2013).
Many studies mainly focused on plant dynamics in the process ofvegetation succession, and few studies simultaneously considered theeffects of plants on soil microbial and soil processes (Guo et al., 2018).Soil microorganisms are important factor in plant-soil interactions(Carbonetto et al., 2014; Zabaloy et al., 2016), participating in andregulating many subsurface ecological processes (Griffiths andPhilippot, 2013; Schulz et al., 2013; Baldrian, 2017), for example, soil
https://doi.org/10.1016/j.ecoleng.2019.105631Received 27 June 2019; Received in revised form 1 October 2019; Accepted 12 October 2019
⁎ Corresponding author at: NO. 26, Xinong Road, Yangling, Shaanxi 712100, China.E-mail address: [email protected] (L. Deng).
Ecological Engineering 142 (2020) 105631
0925-8574/ © 2019 Elsevier B.V. All rights reserved.
T
microbes could affect plant composition and diversity (King et al.,2010; Liu et al., 2010). As an important part of plant-soil system, soilmicrobial community is also strongly influenced by soil and environ-mental factors during vegetation restoration (Zhou et al., 2002;Williams et al., 2013). While the changes of soil quality and vegetationcomposition depends on the direction and duration of restoration(Bardgett and Wardle, 2010). Generally, vegetation responses can in-duce soil development (Alday et al., 2012; Deng et al., 2018b) anddetermine the succession and development of soil microbial community(Klironomos, 2002; Kardol and Wardle, 2010; van der Putten et al.,2013). However, to date, few studies have evaluated the patterns of soilmicrobial community along the vegetation succession chronosequence(Berendsen et al., 2012; Huang et al., 2014).
Previous studies have studied the recovery patterns and composi-tion of soil microorganisms in the process of vegetation restoration (Jiaet al., 2010; Banning et al., 2011; Lopez-Lozano et al., 2013). Studies onbacteria in desert oasis soil have shown that the succession of microbialcommunity was very slow in the short term, which further indicatesthat soil restoration and succession is a long-term process (Lopez-Lozano et al., 2013). Long-term natural succession of vegetation hadsignificantly increased the fungi: bacteria ratio since abandoned farm-land (Jia et al., 2010). Many studies have reported that the dynamics ofsoil bacterial communities were affected by many factors such as mi-crobial biomass (Banning et al., 2011), geological age (Jan et al., 2016),soil properties (Lauber et al., 2008; Cline and Zak, 2015) and plantcommunity composition (Nemergut et al., 2007; Jangid et al., 2013;Zhang et al., 2018). So far, there has little experimental evidence thatpredictable patterns of microbial community structure or compositionoccurred during secondary succession or ecosystem restoration(Banning et al., 2011), because the vegetation restoration system maytake decades, rather than a few years to reach equilibrium (Tanentzapet al., 2009). Various factors need to be considered to explore theoverall trend of soil microbial community during vegetation succession,so that a more flexible recovery strategy can be formulated in the longterm (Jacquet and Prodon, 2009).
In order to understand whether vegetation succession changed soilmicrobial community composition and diversity, we studied the re-lationship between plant and soil characteristics and soil microbialcomposition along with the forest succession of farmland, grassland,shrubland (H. rhamnoides), pioneer forest (P. davidiana) and climaxforest (Q. liaotungensis). In detail, the objectives of this study were toexplore (1) whether the dynamics of soil microbial community areconsistent with plant communities and soil characteristics, and (2) howplants and soils impact soil microbial diversity during vegetation suc-cession. We hypothesized that microbial communities will change withplant and soil properties, and that the functional diversity of microbialcommunities and the abundance of bacterial and fungal will increasethe impact of litter decomposition on soil physical and chemicalproperties. Given the sensitivity of soil microbial community to soil andvegetation variables, we also sought to elucidate which soil and vege-tation characteristics might play an important role in driving the con-tinuous change of microorganisms. Understanding the role of soil mi-croorganisms in material circulation of ecological system, further toprovide theory support in the long process of vegetation restoration anda scientific basis for vegetation restoration and ecological environmentconstruction.
2. Materials and methods
2.1. Study site
The study was conducted on the Lianjiabian Forest Farm of theHeshui General Forest Farm in Gansu (35°03′-36°37′ N, 108°10′-109°18′E) (Fig. 1). The elevation is 1211–1453 m, and the relativeelevation change within the site is approximately 200 m. The meanannual temperature ranges from 7 °C to 8 °C, with the highest
temperature of 36 °C in summer and the lowest temperature of −27 °Cin winter. The mean annual relative humidity is 63% to 68%, and themean annual precipitation is 500 mm to 620 mm, with the maximumprecipitation occurris in the summer from July to September. The studyarea has landforms typical of loess hilly topography. Loessial soil(Calcic Cambisols) is the main soil type, developed from the primary orsecondary loess parent materials, distributed evenly at 50–130 m deepand present on top of a red earth consisting of calcareous cinnamon soil.The study area is located in a temperate zone and is covered in species-rich uniform forests with a forest canopy density ranging between 80%and 95%. This is suitable for the development of deciduous broad-leaved forest and temperate coniferous forest species, such as Populusdavidiana, Betula platyphylla, and Quercus liaotungensis. The shrubs inthe study area are mainly Spiraea salicifolia, Sophora viciifolia and Hip-pophae rhamnoides, and the herbaceous plants are Bothriochloaischaemum, Carex lanceolata, Artemisia gmelinii, Artemisia sieversiana,Artemisia lavandulaefolia.
Grasslands were first developed after farmland abandonment.According to our investigation, the mean vegetation coverage andheight of grasslands are 85% and 0.7 m, respectively. The main vege-tation includes Artemisia gmelinii, Lespedeza bicolor, Bothriochloaischaemum, Setaria viridis, Artemisia capillaries. In the middle stage ofsuccession, the shrubland exhibited a vegetation coverage and height of95% and 5.3 m, respectively, with the only understory vegetation beingCarex lanceolata. P. davidiana is the pioneer species in the region, whilevegetation coverage and height in the climax forest was approximately85% and 18 m, respectively. With Q. liaotungensis as the predominanttree species (Table 1). The main undergrowth plants associated with P.davidiana were Carex lanceolata, Artemisia sp., Ulmus macrocarpa, Acerginnala, Armeniaca sibirica, Betula platyphylla. Understory plants asso-ciated with Q. liaotungensis were Carex lanceolata, Artemisia sp., P. da-vidiana, Betula platyphylla, Pinus tabuliformis.
Ziwuling forest area is the most intact natural secondary forest re-gion in the Loess Plateau. In the Ming and Qing dynasties, vegetationalmost disappeared due to man-made destruction. After the local re-sidents moved out around 1860, arable land in different periods wasabandoned. Therefore, the vegetation naturally recovers on the basis ofabandoned farmland, and the vegetation landscape dominated by thesecondary deciduous broad-leaved forest is gradually formed. At thesame time, the preservation time span of this study area is about150 years, and a relatively complete series of forward succession ofsecondary vegetation is formed in space, namely abandoned farmlandcommunity→herbage community →shrub community→pioneer forestcommunity→climax community. The climax community is Q. liao-tungensis forest (Zou et al., 2002). For comparison, we choose a fieldgrowing Zea mays as the reference site (0 years), as we found that cornwas the only Z. mays in the study area. We also asked local elders andgovernment departments about the history of land use to determine theage of the vegetation communities. The communities we choose forgrassland, H. rhamnoides communities, P. davidiana communities, Q.liaotungensis communities were about 30, 60, 110 and 160 years (Denget al., 2013, 2018b).
2.2. Experimental design and soil sampling
We conducted the field survey from July to August 2017. Fivenatural succession stages of grasslands, H. rhamnoides, P. davidiana, andQ. liaotungensis were selected since farmland abandonment (Deng et al.,2018a). At each succession stage, we choose five sites in the study area.The distance between the sites was no > 5 km apart, and the maximumrelative elevation difference between the two plots was < 120 m. Werandomly established three plots in each community. Sampling plotswere determined by the community size: 20 m × 20 m in the wood-lands, 5 m × 5 m in the shrubs, and 2 m × 2 m in the grasslands andfarmlands. In each sample plot, typical plot methods were used to in-vestigated the plant composition of grasslands, H. rhamnoides, P.
Y. Liu, et al. Ecological Engineering 142 (2020) 105631
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davidiana, and Q. liaotungensis at each successional stage (Vojik andBoublik, 2018). In detail, plant species and density, coverage and heightin each plot were calculated, and study parameters, including diversityindex, richness index and evenness index were calculated. Additionally,five small squares of 1 m × 1 m were selected in the center and fourcorners of each plot. In each square, a soil drill with a diameter of 5 cmwas used to sample from the bottom to the top layer of each sample,taking a total of 5 holes. Mix five types of boring soil in each soil layer(0–10 cm and 0–20 cm), and incorporated into a soil sample, and thenremove plant debris, gravel and other debris through a 2 mm sieve. Soilphysical and chemical indicators were determined after air-drying atroom temperature. When sampled the roots in the above squares, thesurface litter layer was firstly removed, and the root biomass was col-lected along the soil profile with a root drill with a diameter of 9 cm(Deng et al., 2018b). After mixing, the fine roots were obtained throughdry sieving. Three 1 m × 1 m litter collection frames were randomly setup in each sample site, and the litter in the frame was collected reg-ularly. Put the litter in the same three boxes into the plastic bag andtake it back to the laboratory. Separate leaves, fallen branches, fallenfruits, fallen flowers, skin, leaves, miscellaneous branches,
miscellaneous fruits, and crumbs (worms and feces, bird droppings andplant debris) from other parts, and the envelopes are respectivelyweighed in an oven at 80 °C to a constant weight and weighed. In orderto ensure the consistency of samples, the altitude, slope, aspect, andenvironment of each community were basically the same (Table S1).
2.3. Plant diversity calculation method
In this study, we used the following indices to calculate the plantdiversity (Wang et al., 2019a, b):
Shannon-Wiener Diversity Index (H):
=H P Plni i (1)
Patrick Richness Index (Pa):
=Pa S (2)
Pielou Evenness Index (JP):
= HSJP ln (3)
where ʻSʼ is the number of species in the sample and ʻPiʼ is the relative
Fig. 1. The study site and representative vegetation at each successional stage. Farmland, grassland, shrubland (H. rhamnoides), pioneer forest (P. davidiana), andclimax forest (Q. liaotungensis).
Y. Liu, et al. Ecological Engineering 142 (2020) 105631
3
importance of the species within the sample. Relative importance value(Pi) = (relative coverage + relative height + relative abundance)/3.
2.4. Analysis of plant and soil samples
The soil and litter organic carbon (OC) and total nitrogen (TN)concentrations were determined via wet digestion using K2Cr2O7 oxi-dation (Nelson and Sommers, 1982) and the Kjeldahl (Bremner andMulvaney, 1982) method, respectively. Ammonium (NH4
+) and nitrate(NO3
−) concentrations were assayed using Nessler's reagent and thephenol disulfonic acid colorimetric method, respectively (Robertsonet al., 1999). The chloroform fumigation extraction protocol withK2SO4 extraction was employed to determine the soil microbial biomasscarbon (MBC) and nitrogen (MBN) using 10 g of oven-dry equivalentfield-moist soil (Vance et al., 1987). Soil dissolved organic carbon(DOC) and nitrogen (DON) were extracted with K2SO4 (Huffman,1977).
2.5. Illumina MiSeq high-throughput sequencing
Illumina MiSeq sequencing was used to study the communitycomposition of soil bacterial and fungal in each site. The V4-V5 regionof the bacterial 16S ribosomal RNA gene was amplified using primers515F and 907R, and the fungal ITS gene was amplified using primers1737F and 2043R. After PCRs, the PCR products were examined using a2% agarose gel, and the band was extracted and purified using anAxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA,U.S.) according to the manufacturer's instructions and quantified usingQuantiFluorTM-ST (Promega, U.S.). Purified amplicons were pooledbased on concentration and sequenced on Illumina MiSeq platformaccording to standard protocols.
According to the manufacturer's instructions, DNA extractions from0.5 g of soil from each sample were performed using the PowerSoilRDNA Isolation Kit (Mo Bio Laboratories Inc., CA). An aliquot of 10 ng ofpurified DNA template from each sample was amplified in a 25 ml re-action system under the following conditions: initial denaturation at95 °C for 5 min; followed by 30 cycles consisting of denaturation at95 °C for 1 min, annealing at 63 °C for 1 min, and extension at 72 °C for1 min; with a final extension at 72 °C for 5 min. Each sample was am-plified in triplicate, and PCR products were pooled with agarose gelDNA purification kit (TaKaRa, Dalian, China). An equimolar amount ofthe PCR products was combined into one pooled sample and submittedto Majorbio Bio-Pharm Technology for Illumina paired-end (PE) librarypreparation, clustered generation and sequenced by 250 bp PE on anIllumina MiSeq machine. The abundances of total bacteria and fungiwere quantified using real-time PCR based on the 16S rRNA gene andthe internal transcribed spacer (ITS) gene, respectively.
2.6. Processing of sequencing data
The sequences were quality-filtered, and chimera were checkedusing the ‘quantitative insights into microbial ecology’ (QIIME) work-flow (Caporaso et al., 2010). In brief, sequences < 50 bp and readscontaining ambiguous bases or any unresolved nucleotides were re-moved. Sequences with the same barcode were sorted into the samesample. Filter the original flow chart and use UCHIME algorithm toidentify noise and chimeras through the MOTHUR program. The re-maining sequences were clustered by complete-linkage clustering usingthe UCLUST method and assigned to OTUs with 97% similarity. In-dicators of community diversity, including the abundance-based cov-erage estimators (Ace, Chao, Shannon, Sobs), were calculated, andrarefaction curves were obtained using MOTHUR. Finally, the mostabundant sequence in each OTU cluster was selected as the re-presentative sequence and PyNAST method was used for comparison.Using the RDP naive Bayesian classifier, the taxonomic identity of eachphylotype was determined by the bacterial 16S rRNA Silva referenceTa
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Y. Liu, et al. Ecological Engineering 142 (2020) 105631
4
database.
2.7. Data analysis
Statistical analysis of data was conducted using SPSS 22.0. A one-way analysis of variance (ANOVA) followed by Duncan's test was usedto separate the significance differences (P < 0.05) among treatmentmeans, after the homogeneity of variance and normal distribution testswere passed. Redundancy analysis (RDA) was performed to determinethe plant and soil physical-chemical impact factors that had the greatestimpact on soil bacterial and fungal community composition. The mainplant and soil physical-chemical properties affecting soil microbial di-versity were studied by Stepwise Regression Analysis. Use structural
equation model (SEM) to assess how soil and plant factors determinedmicrobial diversity. Drawing was completed using the R 3.4.3 softwarepackage. Microbial community diversity data was analyzed at the levelof phylum.
3. Results
3.1. Dynamics of plant diversity and soil physicochemical properties
The biomasses of litters and roots increased significantly along withthe successional gradient, but their organic carbon and nitrogen con-centrations as well as carbon: nitrogen ratios did not show a significanttrends, but variated with the succession stages (Table 1). Similarly,
Fig. 2. Chemical properties of soil in different soil depths (0–10 cm and 10–20 cm) during the vegetation succession. Note: Values are the mean ± standard error(n= 5). Different letters indicate significant differences (P < 0.05) among the successional stages. S1, S2, S3, S4, and S5 represent farmland, grassland, H.rhamnoides, P. davidiana, and Q. liaotungensis, respectively. SOC: soil organic carbon, TN: total nitrogen, NH4
+: ammonium nitrogen, NO3−: nitrate nitrogen, DOC:
dissolved organic carbon, DON: dissolved organic nitrogen, MBN: microbial biomass nitrogen, MBC: microbial biomass carbon.
Y. Liu, et al. Ecological Engineering 142 (2020) 105631
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species richness and plant diversity also increased significantly duringthe vegetation succession from abandoned farmland to climax forest (Q.liaotungensis stage) (Table 1). Moreover, the concentrations of SOC andTN in the 0–10 cm soil layer had always increased (Fig. 2A and B), andthey were first increased and then decreased in the soil layer of10–20 cm, (Fig. 2A and B). The SOC: TN ratios in the 0–10 soil layerremained basically unchanged, but they were increased first and thentended to be stable along with the forest succession (Fig. 2C). Theconcentration of NH4
+ was the highest in the stage of P. davidiana,while the concentration of NO3
− was the highest in the stage of Q.liaotungensis but the lowest in the grassland, corresponding that thehighest NH4
+: NO3− ratio was associated with the grassland stage, and
the lowest ratio was in the Q. liaotungensis stage (Fig. 2F). The DOC andDON showed an increasing trend with succession stage, and the DOC:DON ratio also increased overall (Fig. 2I). In addition, vegetation suc-cession significantly increased MBC and MBN concentrations(P < 0.05, Fig. 2J and K).
3.2. Dynamics of soil microbial composition and diversity
The predominant fungal phyla from the early-succession stage to thelate-succession stage were Ascomycota (70.9%), Basidiomycota (51.6%),Ascomycota (39.1%), Ascomycota (39.2%), and Basidiomycota (35.3%),respectively (Fig. 3A). The relative abundance of Ascomycota decreasedfirst and then stabilized over time. The relative abundance of Basidio-mycota increased with time and then increased. From early stage to latestage, the predominant bacterial phyla were Proteobacteria (29.1%),Proteobacteria (27.0%), Proteobacteria (31.9%), Firmicutes (38.1%) and
Proteobacteria (30.8%) in the five succession stages (Fig. 3B).The relative richness of the Proteobacteria was relatively stable
during the succession process (Fig. 3A). The number of fungal se-quences increased first and then decreased, with the most occurringduring the H. rhamnoides stage (Fig. 4C). The fungal community wasfluctuating between different succession stages, but there was no sig-nificant difference between different succession stages (P > 0.05,Fig. 4D). With regard to alpha diversity (Fig. 4A and B), the overallsequence of bacterial community was generally fluctuating, and therewas a difference between the succession stages (P < 0.05, Fig. 4A).The fungal community changed and then decreased with time, the trendwas more apparent, and the difference between them was significant(P < 0.05, Fig. 4B). In these two communities, the most diverse se-quence number occurred in the H. rhamnoides stage (P < 0.05, Fig. 4Aand B).
We obtained 1,106,746 16S rRNA and 1,126,240 ITS rRNA se-quences from the samples, with an average of 36,891 and 37,541 genesin each sample. To calculate the diversity index, the OTU was clusteredat a distance of ≤0.03 (approximately 97% of the sequence similarity).For bacteria, with the succession of vegetation, Ace, Chao, Shannon,and Sobs indices did not show a significant trend of increase (P < 0.05,Table 2), but showed a trend of decreasing first, and then increasing. Ingeneral, all rarefaction curves tended to approach saturation with a97% similarity levels, indicating that the amount of data for sequencingreads was reasonable (Fig. 5). In the succession process, the diversity ofplants decreased first, then increased and reached its lowest level at theH. rhamnoides stage, while bacteria and fungi diversity peaked at the H.rhamnoides stage (Table 2).
Fig. 3. Percent of bacterial and fungal community composition in different soil depths (0–10 cm and 10–20 cm) following vegetation succession (A): Fungi, (B):Bacteria. S1, S2, S3, S4, and S5 represent farmland, grassland, H. rhamnoides, P. davidiana, and Q. liaotungensis, respectively.
Y. Liu, et al. Ecological Engineering 142 (2020) 105631
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3.3. Linkages among plant communities, soil properties and the microbialcommunity
RDA analysis showed that the first two axes of bacterial communityaccounted for 61.28% and 8.96% of the total data variation, respec-tively (Fig. 6A). Among them, Firmicutes was the predominant phylumof P. davidiana stage, which was significantly different from the com-munity composition of other restoration stages. And P. davidiana waspositively correlated with litter biomass total nitrogen, the carbon: ni-trogen ratio of the root, NH4
+ and root organic carbon. The bacterialcommunity in the farmland also formed an independent group, which
was mainly correlated to the concentration of root biomass total ni-trogen, DOC and DON, and other vegetation communities were closelyassociated with the Proteobacteria and Actinobacteria (Fig. 6A). Analysisof the fungal community showed that the two axes of RDA accountedfor 69.76% and 12.21% of the total variation, respectively (Fig. 6B). Inaddition, the composition of bacterial community (Fig. 7A) increasedwith the increase of litter organic carbon and root total nitrogen. MBC:MBN had a negative impact on the fungal community (Fig. 7B) and theroot organic carbon and litter total nitrogen had a positive effect on thefungal community composition. Moreover, the carbon and nitrogencontents of litters and roots significantly affected the composition of
Fig. 4. Alpha diversity with bacterial (A) and fungal (B) and Number of bacterial (C) and fungal (D) community sequences in different soil layers (0–10 cm and10–20 cm). Different letters indicate differences between different stages (P < 0.05). Values are the mean ± standard error (n= 5). S1, S2, S3, S4, and S5 representfarmland, grassland, H. rhamnoides, P. davidiana, and Q. liaotungensis, respectively.
Table 2Diversity of bacterial and fungal communities in different soil depths (0–10 cm and 10–20 cm) following vegetation succession.
Treatments Ace Chao Shannon Sobs
0–10 cm 10–20 cm 0–10 cm 10–20 cm 0–10 cm 10–20 cm 0–10 cm 10–20 cm
Bacteria S1 2677.4 ± 28.8ab 2745.4 ± 61.3a 2680.5 ± 27.3a 2737.4 ± 78.9a 6.6 ± 0.0a 6.5 ± 0.0a 2170.7 ± 27.1a 2244.3 ± 27.6aS2 2532.8 ± 26.4bc 2412.9 ± 131.2ab 2467.4 ± 2.2b 2439.3 ± 146.8ab 6.3 ± 0.0b 6.3 ± 0.0b 1957.3 ± 45.7b 1860.7 ± 92.0bcS3 2808.5 ± 47.0a 2734.0 ± 116.4a 2781.6 ± 58.9a 2723.5 ± 126.4a 6.5 ± 0.0a 6.4 ± 0.0a 2232.7 ± 42.6a 2160.0 ± 124.7abS4 2243.2 ± 47.0d 2264.8 ± 115.7b 2229.5 ± 50.7c 2237.8 ± 98.2b 5.5 ± 0.1c 5.5 ± 0.1c 1628.7 ± 80.3c 1642.7 ± 108.8cS5 2498.5 ± 92.8c 2486.1 ± 65.7ab 2473.8 ± 92.4b 2466.1 ± 53.1ab 6.3 ± 0.0b 6.2 ± 0.1b 1973.7 ± 68.7b 1945.7 ± 93.0abc
Fungi S1 625.2 ± 16.7ab 655.6 ± 19.7a 591.7 ± 21.8b 658.8 ± 15.4ab 3.6 ± 0.4a 4.1 ± 0.2a 468.7 ± 30.9b 568.7 ± 27.7aS2 619.4 ± 62.2b 566.8 ± 37.4a 634.2 ± 56.9ab 558.6 ± 43.1b 3.3 ± 0.5a 3.6 ± 0.1ab 526.7 ± 51.3b 490.7 ± 33.3aS3 779.8 ± 56.3a 674.0 ± 45.a 783.7 ± 56.4a 674.0 ± 42.8a 4.2 ± 0.2a 3.8 ± 0.2ab 660.5 ± 8.5a 566.0 ± 20.4aS4 647.2 ± 20.3ab 558.5 ± 44.4a 630.7 ± 19.9ab 569.0 ± 36.4ab 3.3 ± 0.3a 3.2 ± 0.6ab 512.7 ± 21.3b 463.7 ± 64.1aS5 434.1 ± 56.8c 419.2 ± 7.9b 431.1 ± 61.2c 430.3 ± 3.8c 3.0 ± 0.3a 3.1 ± 0.3b 337.0 ± 44.0c 339.0 ± 28.5b
Note: S1, S2, S3, S4, and S5 represent farmland, grassland, H. rhamnoides, P. davidiana, and Q. liaotungensis, respectively.Different letters indicate differences between different stages (P < 0.05). Values are the mean ± standard error (n = 5).
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bacteria and fungi (Figs. 6B and 7, Table 3).
4. Discussion
4.1. Plant and soil characteristics following forest succession
Our study showed that the biomasses of root and litter were in-creased from herbaceous plants to climax forests in the process of ve-getation succession (Table 1). This was consistent with previous find-ings (Miao et al., 2016). Plant coverage and vegetation density alsoincreased with increasing vegetation recovery time (Miao et al., 2016).Although aboveground biomass showed an increasing trend, the LBCand LBN did not change with the changes in vegetation diversity(Table 1), probably because plant community was sensitive to primarysuccession (Li et al., 2016). But as succession progress, all aspects of thecommunity can reach the highest steady state (van der Wal et al.,2006). Moreover, vegetation succession increases litter biomass anddiversity, moving towards a positive trend.
The changes in the plant diversity and biomass indeed to impact onthe soil physicochemical properties (Deng et al., 2018a, 2018b). In thisstudy, most of the restored vegetation types such as SOC, TN, NH4
+,NO3
−, DOC and DON were significantly higher than those of thefarmland (Fig. 2, P < 0.05), confirmed that vegetation restoration canimprove the physical and chemical properties of the soil (Ayoubi et al.,2011; Peng et al., 2013; Deng et al., 2018a). Many studies had alsoshown that soil nutrient concentration increased significantly followingvegetation restoration (Peng et al., 2013; Deng et al., 2017; Jia et al.,2017), mainly due to less human disturbance and higher soil organic
matter input from the corresponding vegetation (Deng et al., 2018a,2018b). Moreover, ground debris from vegetation not only improvessoil properties, such as organic matter and soil structure (Zhao et al.,2017), but it also reduces nutrient losses due to soil erosion (Saviozziet al., 2001). In addition, vegetation types had an impact on soilquality, but soil quality improvement was different for different vege-tation types (Ngo-Mbogba et al., 2015; Yu et al., 2018).
4.2. Soil microbial diversity dynamics following forest succession
The composition of bacteria and fungi had only a quantitativechange, and there was no significant change in species following forestvegetation (Fig. 3). Bacterial community was mainly composed ofProteobacteria, Actinobacteria and Acidobacteria, regardless of the lengthof succession (Kim et al., 2014; Li et al., 2014a, b, c; Lin et al., 2014). Alarge number of Ascomycota and Basidiomycota was observed in thefungal community throughout the succession, indicating that thesefungi were the main decomposers and colonizers of litters during suc-cession. Generally speaking, more substrates are provided in the soilalong with the increase of plant diversity, supporting greater microbialdiversity (Schutte et al., 2010; Lopez-Lozano et al., 2013; Williamset al., 2013). However, the present study have not observed evidencefor such a relationship, although a large number of soil samples wereexamined in which microbial community composition did not changemuch (Fig. 3), where plant effects might be expected to be less pro-nounced (Kuramae et al., 2010).
Our results found that the bacterial and fungal diversity in the earlystage (H. rhamnoides) was higher than that in the later stages, and the
Fig. 5. Rarefaction curves of fungal communities (A, B) and bacterial communities (C, D) based on observed OTUs at 3% distance in different soil depths (0–10 cmand 10–20 cm) for individual samples. S1, S2, S3, S4, and S5 represent farmland, grassland, H. rhamnoides, P. davidiana, and Q. liaotungensis, respectively.
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diversity of fungi during the late stage (Q. liaotungensis) was lower thanthat in the farmland stage (Fig. 4A and B), may be due to strongcompetitors may dominate the later stages during vegetation succes-sion, leading to a decline in microbial species richness (Sun et al.,2018). The Ace, Chao, Shannon and Sobs indexes of fungi and bacteriaeven showed a lower diversity index at the later stage of restorationthan the previous farmland level (Table 2). This may be related to thedegradation of litter by the microbial community and the use of carbonsources, since the carbon source in the litter was closely related to thesoil microbial community (Moller et al., 1999). And pH also had animpact on bacterial and fungal communities (Nicol et al., 2008;Maspolim et al., 2015). Soil pH can limit the activities of soil microbes,reduce microbial degradation activity (Sinsabaugh et al., 2008).Therefore, the composition and diversity of bacterial and fungal com-munities are not significantly different from the pH level at differentstages, and may be related to the species-specific effects of vegetation atdifferent stages on soil and microbial communities (Wyse, 2012).
4.3. Effects of plant communities and soil properties on soil bacterial andfungal diversity
Vegetation and soil characteristics accounted for 61.28% and69.76% of soil bacterial and fungal communities, respectively (Fig. 6),
and LBC, LBN, RBC, RBN, RBC and DOC, DON also had an effect on thecomposition of bacteria and fungal communities (Fig. 7), but microbialcommunities appear to be insensitive to changes in plant diversity(Eisenhauer et al., 2010). It is possible that changes in microbial com-munity lag behind the changes in vegetation and soil following thevegetation succession (Niu et al., 2007). Changes in the diversity ofbacteria and fungal communities did not change as expected. Our ob-servation showed the highest abundance of Firmicutes bacteria wasobserved at the P. davidiana rather than grassland stage seems to con-firm this finding. This result was further explained by Chodak et al.(2016), who reported that no correlation was found between plant di-versity and microbial functional diversity in temperate forests. More-over, short-term studies had found that plant diversity or abundancehas little effect on microbial community composition (Marshall et al.,2011). Our results were similar, although litter biomass and vegetationdiversity vary to varying degrees at different times (Table 2, Table S1),the composition or diversity of bacteria and fungi did not change asmuch as the changes in litter and soil over the succession chronose-quence (P < 0.05, Fig. 3), which may be due to the short duration ofthis study (Marshall et al., 2011).
In addition, the richness of Actinobacteria, Acidobacteria,Proteobacteria, Planctomycetes and Firmicutes was strongly correlatedwith the soil properties of MBC: MBN, NO3
− and NH4+ (Fig. 6A), while
Fig. 6. Redundancy analysis of the relationship be-tween the bacterial community (A), fungi commu-nity (B) and soil properties and vegetation char-acteristics in different soil depths (0–10 cm and10–20 cm). SOC: soil organic carbon, TN: total ni-trogen, MBN: microbial biomass nitrogen, MBC: mi-crobial biomass carbon, RBC: root biomass organiccarbon, RBN: root biomass total nitrogen, DOC: dis-solved organic carbon, DON: dissolved organic ni-trogen, NH4
+: ammonium nitrogen, NO3−: nitrate
nitrogen, LBC: litter biomass organic carbon, LBN:litter biomass total nitrogen. S1, S2, S3, S4, and S5represent farmland, grassland, H. rhamnoides, P. da-vidiana, and Q. liaotungensis, respectively.
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SOC, TN, NO3−: NH4
+, and DOC: DON were related to the richness ofthe dominant Rocellomycota, Basidiomycota, and unclassified-k-Fungi inthe fungal community (Fig. 6B), indicating that soil properties can af-fect the formation of microbial communities. In our study, the con-centration of NH4
+ in soil was strongly correlated with the relativeabundance of Firmicutes in the P. davidiana vegetation stage. The resultsof this study and those of Boyle et al. (2008) found that bacteria werethe main source of NH4
+ minerals in forest soils, and Bottomley et al.(2012) also observed that soil NH4
+ was the main environmental factoraffecting bacterial community structure. Although there was a slightdifference in the MBC: MBN ratio among the different vegetation suc-cession stages (Fig. 2L) and a close relationship between the MBC: MBNand soil substrate (Schindler, 2003). The SOC: TN was also an im-portant factor affecting the soil microbial community and diversity(Hogberg et al., 2007; Hansson et al., 2011). Moreover, higher levels of
soil bacterial and fungal diversity were observed in the middle suc-cession stage (H. rhamnoides) (Fig. 4A and B), further indicating thatsoil nutrient supply may have a positive impact on microbial diversity.Moreover, different soil types can affect vegetation diversity, which inturn affects microbial community diversity (Fry et al., 2017), so the soilproperties such as SOC, DOC, TN, DON also have different effects on thecorresponding bacterial and fungal community structure. There was nosignificant different in the composition and diversity of bacterial andfungal communities at different succession and it may be related to thespecies-specific effects of vegetation on soil and microbial communitiesat different stages (P > 0.05; Wyse, 2012; Rigg et al., 2016). The de-velopment of vegetation succession will lead to the change of unders-tory micro-habitat and become the driving force to change the diversityand composition of vegetation communities (Fry et al., 2017;Bucharova et al., 2016). Changes in microhabitat and vegetation
Fig. 7. Structural equation models based on the ef-fects of MBC, MBN, RBC, RBN, DOC, DON, LBC, LBNon the soil microbial composition, other influencefactors appearing in the article were excluded in astepwise analysis. Arrows with different widths re-present different standardized effect sizes, as shownin the legend. Continuous and dashed arrows in-dicate positive and negative relationships, respec-tively. Significance levels are as follows: ns = notsignificant, *P < 0.05, **P < 0.01. R2 signifies theproportion of variance explained and appears aboveevery response variable in the model. MBN: micro-bial biomass nitrogen, MBC: microbial biomasscarbon, RBC: root biomass organic carbon, RBN: rootbiomass total nitrogen, DOC: dissolved organiccarbon, DON: dissolved organic nitrogen, LBC: litterbiomass organic carbon, LBN: litter biomass totalnitrogen.
Table 3Stepwise regression analysis of the effect of plants and soils on soil microbial diversity.
Community Characteristics Equations Sig.
BacteriaFungi
Ace Ace= 0.892LBC - 41.177MBC:MBN+ 649.636 0.007⁎⁎Chao Chao= 45.893LBN-35.84MBC:MBN+ 1.52RBC+ 265.518 0.003⁎⁎Shannon Shannon= −0.101DON+ 4.129 0.038⁎Sobs Sobs= 1.181RBC+ 45.862LBN- 16.187DON - 41.1MBC:MBN+ 446.135 0.000⁎⁎⁎Ace Ace= 158.931LBN - 6.578DOC+ 2480.809 0.000⁎⁎⁎Chao Chao= 162.158LBN - 7.002DOC+ 2471.568 0.000⁎⁎⁎Shannon Shannon= 0.169RBN - 0.002LBC - 0.02DOC+ 0.187LBN+ 7.102 0.000⁎⁎⁎Sobs Sobs= 168.694LBN - 7.243DOC+ 1917.533 0.000⁎⁎⁎
Note: LBC: litter biomass organic carbon, LBN: litter biomass total nitrogen, MBN: microbial biomass nitrogen, MBC: microbial biomass carbon, RBC: root biomassorganic carbon, RBN: root biomass total nitrogen, DOC: dissolved organic carbon, DON: dissolved organic nitrogen.
⁎ P < 0.05.⁎⁎ P < 0.01.⁎⁎⁎ P < 0.001.
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community composition have profound effects on soil characteristics,such as decreased soil bulk density (supplementary table 1) and in-creased SOC and TN nutrients (Fig. 2). The effects of microhabitats andvegetation communities on soil characteristics during the process ofsuccession can further mediate the diversity and composition of soilmicrobial communities (Benesperi et al., 2012).
Although vegetation diversity had no significant impact on micro-bial community composition, the litter- and root carbon and nitrogenhad an effect on microbial community diversity (Fig. 7A). In particular,RBN and LBC had significant positive effects on bacterial diversity(P < 0.01), confirming that bacterial community composition was af-fected by the nutrient concentration of litters (Yan et al., 2018). LBNand RBC had positive and significant effects on fungal diversity(P < 0.01, Fig. 7B). Our results showed that the chemical properties oflitters are important factors in directly and indirectly predicting mi-crobial changes, and that litters and roots nutrient concentration have agreat impact on microbial community composition (Yan et al., 2018).Therefore, we speculate that the changes in microbial communitycomposition explained by our model might be caused by the nutrientconcentration of the litter. Meanwhile, we also found that soil DOC andDON and MBC: MBN also had negative effects on bacterial and fungalcommunities, respectively (Fig. 7A and B). An increase in litter inputcaused an increase in MBC and MBN levels, suggesting that increasedlitter input resulted in increased microbial activity (Sayer, 2006) andthus a higher rate of decomposition, but that did not cause significantchanges in microbial community, possibly due to microbial diversityaffected by the input of instability carbon components and nutrients(Fang et al., 2015). Earlier studies reported that increasing litters im-proved the concentration of DOC and DON in the soil (Leff et al., 2012),which was roughly consistent with our results. However, the increase ofDOC and DON in soil will cause a decrease of bacterial and fungalcommunity diversity (Fig. 7), because the leaching of DOC and DONfrom the litter layer and their retention in the mineral soil was acomplex process (Crow et al., 2009). Further studies are needed tounderstand the changes in soil DOC following litter abundance in ourstudy site.
5. Conclusion
Plant diversity and soil quality decreased in the early stage afterfarmland abandonment, however, forest succession significantly in-creased plant diversity and soil quality in the long-term and there wasno significant change in microbial community composition, indicatingthat the changes in plant, soil and soil microorganisms were not syn-chronized with each other. In addition, even though there has no sig-nificant change in soil microbial community composition, forest suc-cession will change the micro-habitats under the forest and have a long-term and positive effect on soil microbial activity. The study have de-monstrated that the forest succession can improve soil quality and af-fect microbial composition to some extent. In the future, we need toconsider the vegetation succession mechanism to strengthen the studyon the effects of soil microbes in the process of vegetation restoration.At the same time, natural restoration is a relatively slow ecologicalprocess to some extent. Therefore, it is of great significance to accel-erate the vegetation restoration process by making full use of the ve-getation succession rules, conducting appropriate human interferenceand shortening the succession time, which is of great guiding sig-nificance to the sustainable management of forest resources.
Declaration of Competing Interest
We declared no conflict of interest exits in the submission of thismanuscript, and the work described was original research that has notbeen published previously, and not under consideration for publicationelsewhere, in whole or in part.
Acknowledgments
The study was supported by the National Natural ScienceFoundation of China (41877538, 41771549), the Strategic PriorityResearch Program of the Chinese Academy of Sciences(XDA23070201), the Funding of Special Support Plan of Young TalentsProject of Shaanxi Province in China, and the Funding of PromotingPlan to Creative Talents of ‘Youth Science and Technology Star’ inShaanxi Province of China (2018KJXX-088).
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.org/10.1016/j.ecoleng.2019.105631.
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