Plant and soil traits driving soil fungal community due to tree plantation on the Loess Plateau

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Plant and soil traits driving soil fungal community due to tree plantation on the Loess Plateau Yang Yang a,b,c, Huan Cheng d, Yanxing Dou a, Shaoshan An a,⇑ a

State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling 712100, China State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi’an 710061, China c CAS Center for Excellence in Quaternary Science and Global Change, Xi’an 710061, China d Department of Biology, University of Maryland, College Park, MD 20742, USA b

h i g h l i g h t s

g r a p h i c a l a b s t r a c t


Soil fungal community was mediated

synergistically by the plant and soil traits.
Tree plantation had a large effect on soil fungal community compared to natural restoration.
Plant and soil traits co-explained soil fungal community in terms of tree plantation.
The link among plants, soil and fungal community were built, but need further studies.

a r t i c l e

i n f o

Article history: Received 21 April 2019 Received in revised form 31 August 2019 Accepted 18 September 2019 Available online xxxx Keywords: Plant traits Soil traits Fungal community Tree plantation High-throughput sequencing Loess Plateau

a b s t r a c t It is widely accepted that soil fungi plays a crucial role in biogchemical cycle in terrestrial ecosystems, and soil fungal community can be shaped by plant and soil traits; however, we still know very little about the combined impacts of plant and soil traits on soil fungal community due to tree plantation, especially on the Loess Plateau. In doing so, we provided a conceptual framework bridging knowledge on plant, soil traits and soil fungal community, which tested the combined impacts of plant and soil traits on soil fungal community due to tree plantation compared with natural restoration (CK) on the Loess Plateau. There was a disproportionate influence of tree plantation on soil fungal community by using nonmetric multidimensional scaling (NMDS) (p < 0.05) and the interaction networks. Additionally, soil organic carbon (SOC), soil pH, C/N, biomass in litter and root were highly related to the dominant soil fungal community (such as Ascomycota and Basidiomycota), which can be considered as the main drivers for soil fungal community. Most importantly, litter traits and root traits were considered as the key predictors in shaping soil fungal community in terms of tree plantation (especially litter and root C/N), while soil traits and root traits were considered as the key predictors in terms of natural restoration. Besides, structural equation modeling (SEM) indicated that soil fungal community was co-mediated by soil and plant traits due to tree plantation, and the total effects of soil traits, plant traits, litter traits and root traits on soil fungal community were higher in tree plantation, suggesting that tree plantation had a large effect on soil fungal community compared to natural restoration. Finally, we build a conceptual framework to clarify the

⇑ Corresponding author at: State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University. Rd. Xinong No.22, Yangling, Shaanxi 712100, China. E-mail address: [email protected] (S. An). https://doi.org/10.1016/j.scitotenv.2019.134560 0048-9697/Ó 2019 Elsevier B.V. All rights reserved.

Please cite this article as: Y. Yang, H. Cheng, Y. Dou et al., Plant and soil traits driving soil fungal community due to tree plantation on the Loess Plateau, Science of the Total Environment, https://doi.org/10.1016/j.scitotenv.2019.134560

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combined impacts of plant and soil traits on soil fungal community, providing a new sight to understand the crucial role of plant and soil traits in shaping soil fungal community due to tree plantation, and the interactions among plant and soil and also soil fungal community need further studies. Ó 2019 Elsevier B.V. All rights reserved.

1. Introduction Soil fungi, one of the most abundant soil microbe, plays a crucial role in soil nutrient recycling in terrestrial ecosystems (Kohler et al., 2017; Zhu et al., 2017). It is widely accepted that soil fungi controls the main soil biogchemical processes, which is highly co-regulated by plant and soil properties, and the composition of soil fungal community is also shaped by plant and soil traits (Duru et al., 2014; Thompson et al., 2017). However, the roles of plant and soil traits in shaping soil fungal community are still largely unknown recently. On the one hand, plant traits, such as plant richness (number of species) and plant cover, made a large contribution to the richness of soil fungal community (Goodness et al., 2016; Jiang et al., 2017). In fact, plant richness and plant cover promoted large numbers of litters and roots which provided more energy to soil fungi (Geml and Wagner, 2018; Hu et al., 2018; Tong et al., 2018), and the root directly produced more exudates to promote soil nutrients absorption for soil fungi (Laforestlapointe et al., 2017; Mariotte et al., 2018). On the other hand, soil traits, including soil physical–chemical properties, soil nutrients and some micro-elements, which are beneficial to form the soil fungi-dominated networks, leading to the strong nutrient cycling and litter decomposition (Jiang et al., 2017; Chen et al., 2018). Although increasing numbers of studies demonstrated that plant and soil traits governed the composition of soil fungal community (Piqueray et al., 2015; Mariotte et al., 2018), we still know very little about how plant and soil traits co-explain soil fungal community. Thus the interactions among plant and soil and also soil fungal community need further study on the Loess Plateau. The Loess Plateau in China is one of the most eroded areas and deepest loess deposits in the world (Fu et al., 2017). To reduce water loss and soil erosion, the Chinese Government launched a series of remarkable projects science 1980s (Chen et al., 2015; Feng et al., 2016). Thereafter, the Grain-for-Green project largely contribute to maintaining soil conservation, improving carbon (C) sequestration, and reducing floods since 1999 (Fu et al., 2017). Indeed, a large area of abandoned farmland has been considered to tree plantation (Li et al., 2017; Tong et al., 2018). Actually, tree plantation has been regarded as an effective measure for promoting ecosystem restoration by mitigating carbon dioxide (CO2) concentrations in the atmosphere (Li et al., 2017; Nave et al., 2018), accelerating soil C sequestration and leading to the increase of soil organic C (Li et al., 2012; Lange et al., 2015; Lal, 2018). In contrast to tree plantation, natural restoration promotes the ecosystem biogeochemical cycles (particularly soil microbial community activity) by the natural enclosure measures (Li et al., 2017; Tong et al., 2018). There are several results from published quantitative papers demonstrated that soil microbial communities were induced to a large change by tree plantation on the Loess Plateau (Ke et al., 2015; Zhang et al., 2016; Calderón et al., 2017; Yang et al., 2018). Increasing evidences confirmed that soil microbial communities were positively influenced by tree plantation, particularly nitrogen-fixing bacteria or some special fungi (Niu et al., 2017; Prober et al., 2015). For instance, Yang et al. (2018) declared that soil C storage had a strong correlation with soil bacterial diversity in this region. Similarly, soil nutrients (Zhang et al., 2016) or the other environmental variables (Karhu et al., 2014;

Laforestlapointe et al., 2017; Geml and Wagner, 2018; Tripathi et al., 2018) had a large contribution to soil microbial community. Further, much more studies focused on soil fungal community due to tree plantation, and we summarized a conceptual figure of the combined impacts of plant and soil traits on soil fungal community due to tree plantation on the Loess Plateau from previous studies (Fig. 1). For example, C inputs and flows from plant biomass to litter, root and soil due to tree plantation, and then utilized by soil microbes. In the process of C flowing, soil traits, plant traits, litter traits and root traits which related to C inputs are important to drive soil microbial community. Therefore, a key issue need to be paid more attention to test the combined impacts of plant and soil traits on soil fungal community due to tree plantation on the Loess Plateau. Here, we sought to test the combined impacts of plant and soil traits on soil fungal community due to tree plantation, providing a new sight to understand the crucial role of plant and soil traits in shaping soil fungal community on the Loess Plateau. Compared with tree plantation, natural restoration were investigated and regarded as CK, and soil fungal community composition and diversity were measured by high-throughput sequencing approaches. Besides, soil traits, litter traits and root traits were also measured to explain the change of soil fungal community. In doing so, we build a conceptual framework to test the combined impacts of plant and soil traits on soil fungal community in this region. 2. Methods 2.1. Study sites This work was conducted in Nanxiaohe watershed, located in Gansu Province. There were two watersheds (Dongzhuanggou, DZG and Yangjiagou, YJG) which have the similar geological and topographical backgrounds in this region (Supplementary Fig. 1). However, DZG has been fenced by the Government since 1954, and now, the dominant plant species are Artemisia vestita, Cleistogenes squarrosa, Stipa bungeana and Arenariae radix. By contrast, YJG is mainly conducted by tree plantation which occurred from 1954 to 1958, and the dominant trees are Pinus tabuliformis, Prunus sibirica, Robinia pseudoacacia and Malus pumila. The study site occupies a semi-arid continental climate. The mean annual temperature and precipitation are 9.3 °C and 556.5 mm, respectively. More than 65% of the annual precipitation occurred from June to September between the period of 2000 and 2017. The altitude of this study site is between 1050 and 1423 m with the silt-loamy. 2.2. Sampling design The field survey was conducted in 2017 in July. Four kinds of tree plantation (Pinus tabuliformis, Prunus sibirica, Robinia pseudoacacia, and Malus pumila) and four kinds of grassland (CK) were selected. In each vegetation type, we established three samples (50  50 m), which were located at least 50 m apart from each other. These samples were nested in a 200 m2 subplot (10  20 m) and 15 m2 subplot (1  1 m) by a modified Whittaker plot. We sampled soil from the surface layer (0–20 cm). Five soil replicates were obtained with a soil core (6 cm in diameter) along an S-shaped curve and then mixed in one sample. We divided soil

Please cite this article as: Y. Yang, H. Cheng, Y. Dou et al., Plant and soil traits driving soil fungal community due to tree plantation on the Loess Plateau, Science of the Total Environment, https://doi.org/10.1016/j.scitotenv.2019.134560

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Fig. 1. Conceptual figure of the impacts of plant and soil traits on soil fungal community in terms of tree plantation on the Loess Plateau.

sample into two parts: one part dried for 15 days and sieved through a 2-mm mesh to analyze soil nutrients. For the other soil sample, the gravel and roots were removed and stored at 80 °C immediately for DNA analysis, by using liquid nitrogen. In addition, we harvested above-ground biomass and litters in each sample (0.5  0.5 m) to obtain the dry plant biomass, which was collected from the ground surface. The above-ground biomass and litters were oven-dried at 80 until the constant weight (about 48 h) and then weighed to the nearest milligram. Further, we investigated the plant richness (PR), and a ruler (0.1 cm) was used to measure litter thickness (LC). The living roots were distinguished in these samples and isolated using a 2-mm sieve, and the fine roots remained. The roots in each sample were washed and measured the total length (cm) by a scanner, and then dried and measured root biomass (RB).

measure soil organic carbon (SOC, gkg1) and soil total nitrogen (STN, gkg1). Both soil NO–3-N and soil NH+4-N were determined by using a Seal Auto Analyzer (AA3 HR, Germany). Finally, we used the fumigation-extraction method to measure soil microbial biomass C (SMBC, mgkg1) and soil microbial biomass N (SMBN, mgkg1), and the correction coefficient was 0.35 (for SMBC) and 0.4 (for SMBN) (Brookes et al., 1985).

2.5. Soil total DNA analysis First, each soil DNA sample was extracted by using a Fast DNA SPIN kits (MP Biomedicals, USA) according to the standardized instructions and then stored at 80 for the further analysis. Second, each soil DNA sample was quantified by spectrophotometer (Waltham, MA, USA) and dissolved by TE buffer (50 ml).

2.3. Plant traits We selected the main plant traits (at least six replication), which were used to explain soil fungal community, including leaf dry matter content (LDMC, gkg1), leaf carbon and nitrogen concentration (LCC, LNC, %). Additionally, litters were singly collected, and the plant richness (PR) (the number of species) and plant biomass (PB) were investigated in each plot carefully. Plant height: A ruler (0.1 cm) was used to measure the absolute height; LDMC: washing 20 min and then drying 48–72 h (85 °C); LCC, LNC, RC, RN, LC, LN: Samples were smashed, passed through a 1.5-mm sieve and then measured by an elemental analyzer (vario Macro cube, Elementar, Germany). In addition, the plant and soil traits were presented in Supplementary Table 1. 2.4. Soil traits Soil pH was measured in a 1:1.5 soil water extraction (v/v). The potassium dichromate oxidation (Nelson and Sommers, 1982) and Kjeldahl methods (Bremner and Mulvaney, 1982) were used to

2.6. DNA extraction and PCR amplification A quantitative PCR (qPCR) assay specific for soil fungal internal transcribed spacer (ITS) region was amplified depending on the primer set: ITS1F: 50 -GGAAGTAAAAGTCGTAACAAGG-30 and ITS2F: 50 -GCTGCGTTCTTCATCGATGC-30 . In the PCR process, PCR components include 2 ll of dNTPs, 2 ll of DNA template, 0.25 ll of Q5 High-Fidelity DNA Polymerase, 5 ll of Q5 reaction buffer, 5 ll of Q5 High-Fidelity GC buffer, and 8.75 ll of H2O and reverse primer. The q-PCR was conducted in triplicate under the following conditions: denaturation at 95 °C for 30 s, extension at 75 °C for 30 s, annealing at 55 °C for 30 s, and then extension at 75 °C for 600 s. PCR amplicons from each soil sample were pooled in the same concentration; thereafter, the primers were purified by 1% Agarose Gel DNA (TaKaRa). Each soil sample was mixed in equal amounts and sequenced on an Illumina MiSeq sequencing machine (Roche Diagnostics Corporation, Branford, USA). High-throughput sequencing data has been deposited by Shanghai Majorbio BioPharm Technology Company (Shanghai, China).

Please cite this article as: Y. Yang, H. Cheng, Y. Dou et al., Plant and soil traits driving soil fungal community due to tree plantation on the Loess Plateau, Science of the Total Environment, https://doi.org/10.1016/j.scitotenv.2019.134560

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2.7. Processing of sequencing data High-throughput sequencing reads of all soil samples were assigned to valid sequences with a series of software on the Galaxy platform (Lundberg et al., 2013). The lower-quality sequences were filtered, including these sequences with mononucleotide repeats, which were more than 8 bp or had a length lower than 150 bp. Paired-end reads were assembled using FLASH. After chimera detection, the remaining high-quality sequences of OTUs (operational taxonomic units, at 97% sequence) were clustered by UCLUST, and OTUs containing<0.001% of total sequences were discarded. (Altshuler et al., 2000). Depending on the default parameters, the representative sequence was selected by using PyNAST to perform a UCLUST analysis. Then, an OTU table was further generated to record the abundance of each OTU in each soil sample and the taxonomy of these OTUs. 2.8. Statistical analysis The homogeneity of the data variances was examined, and the Fisher’s test was conducted at the p < 0.01 and p < 0.05 level by using SAS 9.3. We calculated soil OTU-level microbial diversity indices (Chao1 richness, Shannon diversity index, ACE and Simpson index) using the OTU table in Quantitative Insights Into Microbial Ecology (QIIME, version 1.8.0) (http://qiime.sourceforge.net) (Turnbaugh et al. 2009). The OTU-level-ranked abundance curves were generated to compare the richness and evenness of the OTUs across all soil samples (Chao, 1984; Faith, 1992). Spearman’s correlation coefficients among soil microbial diversity were calculated, which were used to build interaction networks among soil fungal community using MEGAN (McArdle and Anderson, 2001). Furthermore, the significance of soil fungal community following vegetation restoration was assessed by analysis of similarities (ANOSIM) and permutational multivariate analysis of variance (PERMANOVA). Based on the nonparametric distance-based linear regressions, we used multi-model inference approaches to evaluate whether plant and soil traits explained well to soil fungal community and diversity (Delgadobaquerizo et al., 2018). The Euclidean distance about the pairwise taxonomic distance (Bray-Curtis) was calculated using the R package ‘‘vegan” (https://www.r-project.org/, Version v.3.2.2) and was fitted on the NMDS graph (Yang et al., 2018). Based on the Bray-Curtis distances, a detrended correspondence analysis (DCA) and unweighted pair-group method (UPGMA) were used to describe soil fungal community. In addition, we identified the most important predictors to soil fungal community by using random forest analyses (Baldrian et al., 2012). Structural equation modeling (SEM) was constructed, and the path coefficients were calculated after 999 bootstraps in AMOS (version 20.0). In SEM, we selected the specific variables that depend on Akaike information criterion (AIC) and the root mean square error of approximation (RMSEA, p < 0.05); then, we obtained the indirect and direct effects from the interaction pathways. Finally, we assessed the fitting goodness by using the chi-squared tests.

restoration (p < 0.05) (Supplementary Table 2). Moreover, soil fungal sequences can be grouped into 2779 phylotypes, and Ascomycota, Basidiomycota and Zygomycota were the dominant phyla (relative abundance greater than 5%) at 97% similarity level, accounting for more than 80% of all sequences, belonging to 70 phyla, 145 classes, 405 orders, 666 families and 907 genera. Further, the Good’s coverage was above 0.95, and the vast majority of soil microbial community showed that natural restoration > tree plantation. As for Chao1 richness, we found that the lowest Chao1 richness was in Pinus tabuliformis, and there was a large difference between tree plantation and natural restoration. Similarly, the ACE index showed the same trend. In addition, the dominant abundant soil fungal phyla were Ascomycota and Basidiomycota (Supplementary Table 3). Other phyla, including Glomeromycota, Chytridiomycota, Rozellomycota and Neocallimastigomycota, had a lower explanation for soil fungal community. Further, soil fungal community showed significant differences between natural restoration and tree plantation at the phylum level, including a greater relative abundance of Basidiomycota (14.6% vs. 7.1%) and Zygomycota (5.8% vs. 4.0%) in tree plantation and a greater relative abundance of Ascomycota (54.1% vs. 59.2%) in natural restoration. Besides, Glomeromycota, Chytridiomycota, Rozellomycota and Neocallimastigomycota presented a lower abundance across all soil samples. In Fig. 2, we found that soil fungal community was dominated by Ascomycota in natural restoration, and Basidiomycota in tree plantation at the class level, and both tree plantation, natural restoration and their interactions had a large effect on Ascomycota and Basidiomycota (p < 0.01). As for soil fungal diversity (including Shannon–Wiener diversity, the number of OTUs, Simpson’s diversity), which was determined by the Heip’s evenness index and Chao1 richness, and calculated by the most sequences per soil sample (Supplementary Table 2). There was a significantly difference about Shannon– Wiener index between natural restoration and tree plantation (two-way ANOVA, p < 0.01). Nevertheless, there was no significant difference in Simpson (p greater than 0.05). Additionally, the results of two-way ANOVAs indicated that both natural restoration and tree plantation had a large effect on the number of OTUs and Shannon-Wiener index (p < 0.001) (Supplementary Table 4). Neither natural restoration nor tree plantation affected the Simpson’s diversity index (p = 0.072, p = 0.063, respectively). Besides, tree plantation had no significant effect on soil fungal diversity (p = 0.063, p = 0.081, respectively); however, natural restoration revealed a significant effect on soil fungal diversity (p < 0.05). To determine the association of soil fungal community structure between tree plantation and natural restoration, we profiled the structural changes of soil fungal community by using nonmetric multidimensional scaling (NMDS) according to the BrayCurtis dissimilarities (Supplementary Fig. 2 A). NMDS ordinations indicated that soil fungal community was subjected to natural restoration and tree plantation. In contrast, soil fungal community in natural restoration clustered away from tree plantation. In addition, the rarefaction curves of fungal community are presented in Supplementary Fig. 2 B in terms of the total number of OTUs. 3.2. Network of fungal community composition due to tree plantation

3. Results 3.1. Soil fungal community due to tree plantation Across all soil samples, a total of 331,001 (from 38,723 to 45716) fungal sequences were obtained, as shown in Supplementary Table 1. Soil fungal abundances of tree plantation and natural restoration varied from 40,049 to 45,716 and 38,723 to 42,104 across all soil samples, respectively, and fungal sequences revealed no significant difference between tree plantation and natural

The dominant fungal taxa (OTUs) were determined by using the unweighted pair-group method (UPGMA), and the dominant fungal taxa for natural restoration and tree plantation are presented in Fig. 3. We found that the relative abundance of the dominant soil fungal taxa were different at the phylum level (Supplementary Table 5). Further, soil fungal taxa of tree plantation were closely related to OTU2628 (p_Ascomycota), OTU3961 (f_Nectriaceae), and OTU3387 (f_Clavariaceae). The relative abundance of these OTUs was much greater in natural restoration, and these fungal

Please cite this article as: Y. Yang, H. Cheng, Y. Dou et al., Plant and soil traits driving soil fungal community due to tree plantation on the Loess Plateau, Science of the Total Environment, https://doi.org/10.1016/j.scitotenv.2019.134560

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Fig. 2. The relative abundance of dominant fungal taxa in terms of tree plantation. ‘‘Others” include phyla with a relative abundance <1% on the class level for fungi. Differences in fungal diversity between tree plantation and natural restoration. All data are presented as the mean standard error. Asterisks indicate that a horizon has significant influence (** indicates p < 0.01; * indicates p < 0.05; ns indicates no significantly difference.

taxa were closely related to OTU5381 (p_Ascomycota), OTU7107 (g_Ceratobasidium) and OTU6901 (g_Pleurosticta). Based on the significant Spearman’s correlation coefficients, we constructed the co-occurrence patterns and paths of the dominant taxa of soil fungal community using a network analysis (Supplementary Table 6). Notably, there were more positive correlations than negative correlations in all networks, both in tree plantation and natural restoration (Table 2). The networks of soil fungal community in tree plantation were stronger than natural restoration (more abundant nodes). In particular, Basidiomycota showed strong positive correlations compared with other fungal taxa, and we found a typical module structure due to the calculated modularity index, which was more than 0.4, and the average clustering coefficient (avgCC), the values of the average path length in these empirical networks were higher. Then, we concluded that natural restoration and tree plantation had a remarkable effect on soil fungal community. Specifically, the modularity index and average connectivity (avgK) was higher in networks, while the average path length showed the opposite trend. In addition, the number of nodes in tree plantation was more than natural restoration networks, suggesting that tree plantation had a large effect on soil fungal community than natural restoration. 3.3. Influence of soil traits, plant traits on soil fungal community due to tree plantation We got the strong interactions of soil fungal community in natural restoration and tree plantation by PERMANOVA analysis (p < 0.05) (Supplementary Table 4). The Mantel test indicated that soil traits, plant traits, litter traits and root traits significantly affected soil fungal community in natural restoration and tree plantation, and the correlation coefficient of tree plantation was higher than natural restoration (Supplementary Table 7). In addition, effects of root traits and soil traits were more sensitive to soil fungal diversity, especially in tree plantation. We used Spearman correlation coefficients to link the dominant fungal phyla and soil traits, plant traits, litter traits and root traits

(Supplementary Table 7). The results showed that soil pH, LDMC, LNC were significantly negatively correlated with the dominant fungal phyla (Spearman correlation, p < 0.05). The other soil traits, such as plant traits, litter traits and root traits were strongly positively correlated with Ascomycota and Basidiomycota. Totally, the dominant fungal phyla were significantly influenced by root traits and soil traits in tree plantation and natural restoration. Besides, we found that litter traits and root traits explained more variation in tree plantation, while soil traits and root traits explained more variation in natural restoration (Table 2). According to the variation in the dominant fungal phyla for DCA based on the BrayCurtis distances (Fig. 4 A, B), the findings were similar to the the results of Spearman correlation. From the results of Table 3, soil pH, SOC, and C/N were regarded as major predictors for soil fungal community through the best goodness of fit based on the variation partitioning modeling. Thus, soil pH, SOC, C/N, biomass in litter and root were regarded as the main predictors for soil fungal community in natural restoration and tree plantation, which was agreement with the results from correlation analysis. Additionally, we used the random forest analyses to identify the key role of soil traits, plant traits, litter traits and root traits to shape with soil fungal community (Fig. 5). In tree plantation, litter traits and root traits were considered the key predictors in shaping soil fungal community, while soil traits and root traits were considered the key predictors in shaping with soil fungal community in natural restoration. 3.4. Plant and soil traits driving soil fungal diversity due to tree plantation We used SEM (Supplementary Fig. 3) to understand the effects of soil traits, plant traits, litter traits and root traits on soil fungal diversity. In tree plantation, fungal diversity fitting goodness was 0.875, indicating that this model could explain soil fungal diversity well (p < 0.01) (Fig. 6), and there are significant SEM paths in tree plantation (F = 32.13, v2 = 0.014, GFI = 0.043, df = 24, p < 0.01, AIC = 128.32, RSMEA = 0.017, and 59.16%). Similarly, in natural

Please cite this article as: Y. Yang, H. Cheng, Y. Dou et al., Plant and soil traits driving soil fungal community due to tree plantation on the Loess Plateau, Science of the Total Environment, https://doi.org/10.1016/j.scitotenv.2019.134560

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Fig. 3. Interaction networks, and a connection stands for a strong and significant correlation in tree plantation (A) and natural restoration (B). The co-occurring networks are colored by phylum. Circles represent OTUs, and the size of each circle represents its relative abundance. For each panel, the size of each node is proportional to the number of connections (that is, degree), and the thickness of each connection between two nodes (that is, edge) is proportional to the value of Spearman’s correlation coefficients. A red edge indicates a positive interaction between two individual nodes, while a blue edge indicates a negative interaction. In addition, the unweighted pair group method using arithmetic averages (UPGMA) was used to compare the indicator species in tree plantation (C) and natural restoration (D). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

restoration, fungal diversity goodness of fit was 0.801, suggesting a well explained model (p < 0.01), with significant SEM paths (F = 31.54, v2 = 0.017, GFI = 0.031, df = 24, p < 0.01, AIC = 117.89, RSMEA = 0.021, and 53.04%). In the final SEM, we get the indirect, direct, and the total pathway effects of tree plantation and natural restoration on soil fungal diversity in detail. For example, tree plantation indirectly affected soil fungal diversity by soil traits (0.604) and directly affected by root traits (0.814), while there was a negative effect in plant traits and litter traits (the total effect were 0.189 and 0.366, respectively). Likewise, natural restoration directly affected soil fungal diversity by soil traits (0.483) and indirectly affected soil fungal diversity by root traits (0.787), while there was a negative effect on plant traits (-0.187) and litter traits (-0.062). In contrast, the total effects of soil traits, plant traits, litter traits and root traits were higher in tree plantation.

4. Discussion 4.1. Soil fungal abundance and community due to tree plantation We profiled the patterns of soil fungal community by using nonmetric multidimensional scaling (NMDS) according to the Bray-Curtis dissimilarities (Supplementary Fig. 2). The NMDS ordinations revealed a large effect of natural restoration and tree plantation on soil fungal diversity, and this result was confirmed by PERMANOVA analysis (Supplementary Table 4). Specially, ACE, Chao1, Shannon indices and soil fungal networks in natural restoration were higher than tree plantation (Fig. 2). These findings emphasized the significance of vegetation restoration on soil fungal diversity in this region (Zhang et al,. 2016; Yang et al,. 2018). In addition, the patterns of soil nutrients had the similar change trend with soil fungal diversity (Supplementary Table 1). Gener-

ally, soil fungal community obtains available natural resources with a specific ecological niche, which contributes more to the decomposition of SOC (Sun et al., 2016; Zhang et al., 2018). In turn, the decomposition of SOC provides a suitable habitat and enough energy for soil fungal community (Yuan et al., 2015; Kyaschenko et al., 2017; Zhang et al., 2016). For instance, the complex of fungal community composition and the higher diversity promoted nutrient absorption (Karhu et al., 2014; Brabcová et al., 2018; Tripathi et al., 2018), thus soil nutrients showed the similar change trend with soil fungal diversity. Accordingly, our results showed that soil fungal community was dominated by Ascomycota (Fig. 3), which was consistent with previous studies (Zhou et al., 2017; Wang et al., 2018), indicating that Ascomycota can be considered as the primary indicator species for soil fungal community, and this was confirmed by interaction networks (Fig. 2; Supplementary Table 5). Previous studies reported that soil fungal community was subject to adaptation to strong pressures in tree plantation (Li et al., 2017; Yang et al., 2017a,b; Brundrett and Tedersoo, 2018). The possible reason was the large root-associated soil fungi and the lower soil pH from the rhizosphere, supporting the higher soil nutrients and more complex of soil fungal interaction networks (Chen et al., 2016; Fan et al., 2018; Hong et al., 2018; Põlme et al., 2018; Tripathi et al., 2018), and this result was further confirmed by Spearman correlation analyses (Supplementary Table 6; Supplementary Fig. 3). For example, tree plantation had a large and complex root system which provided a cosy habitat for soil fungal community, and thus promoted large soil nutrient absorption (Zhou et al., 2017; Mommer et al., 2018). These findings implied that tree plantation was beneficial to regulate soil fungal community compared to natural restoration. Generally, soil fungi is regulated by the inter-species networks rather than living in isolation (Kerfeld et al., 2018; Mmm et al.,

Please cite this article as: Y. Yang, H. Cheng, Y. Dou et al., Plant and soil traits driving soil fungal community due to tree plantation on the Loess Plateau, Science of the Total Environment, https://doi.org/10.1016/j.scitotenv.2019.134560

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Fig. 4. Variation in the dominant fungal phyla for the detrended correspondence analysis (DCA), which are constrained by tree plantation (A) and natural restoration (B) and based on Bray-Curtis distances. DCA axes 1 and 2 explained 53.2 and 32.1%, respectively. The arrows point to the centroid of the constrained factor. Vectors represent soil traits, litter traits, plant traits and root traits plotted in ordination space. Circle sizes correspond to the abundance of the dominant fungi, and colors are assigned to different phyla. In addition, heat maps showing the mean abundance of soil fungi in tree plantation (C) and natural restoration (D). Pink and green colors indicate high and low relative abundances, respectively. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

2018; Poole et al., 2018). In this way, we found that the networks (in terms of clustering coefficient, modularity, geodesic distance in network models) of soil fungal community had significant differences between tree plantation and natural restoration (Table 1). Specifically, networks of tree plantation had 8% more links but 5% fewer nodes than natural restoration, and networks of natural restoration had a greater neighborhood connectivity (735 vs 1078) and mean value connectivity (13.26 vs 20.47) than tree plantation. Further, we found a typical module structure due to the calculated modularity index, which was more than 0.4, and the average clustering coefficient (avgCC), the values of the average path length in these empirical networks were higher, indicating that these networks were modular, supporting previous studies (Koskella et al., 2017; Belin et al., 2018). 4.2. Influence of soil traits, plant traits on soil fungal community due to tree plantation Both soil traits, plant traits, litter traits and root traits played the key roles in shaping soil fungal community. In DCA, soil fungal community was influenced by plant and soil traits, which is similar with previous studies (Yang et al. 2018). In addition, SOC, soil pH, C/N in soil, biomass in litters and roots were considered as the

main driver for soil fungal community regardless of tree plantation and natural restoration (Fig. 4; Supplementary Table 6). Thus, an interesting question remained: how did SOC, soil pH, C/N in soil traits, biomass in litters and roots co-regulate soil fungal community due to tree plantation? In fact, SOC, C/N in soil, biomass in litters and roots greatly contributed to the richness of soil fungal community (Chen et al. 2016; Fan et al. 2018; Hong et al. 2018), and SOC provided more nutritional resources for soil microbes via litter decomposition (Yang et al., 2017a,b; Wang et al. 2018; Põlme et al. 2018). Further analysis showed the significant correlations between SOC, C/N and soil fungal community groups, such as Ascomycota and Basidiomycota. Specifically, leguminous plants (Robinia pseudoacacia) in terms of tree plantation, consistently assimilated more nutrients because of the large biomass in litters and roots, resulting in a dramatic increase of soil fungal diversity (Geml and Wagner, 2018; Tripathi et al. 2018). Besides, soil NO–3N and NH+4-N were both highly related to soil fungal diversity, whereas STN was not significantly related to soil fungal diversity, suggesting that not all N elements (STN, NH+4-N, NO–3-N) contribute to soil fungal diversity. Given the reason that N availability was strengthened in its available forms (e.g., NO–3-N, NH+4-N) by soil fungal community, and these available N forms promoted the activity of soil fungal community and leaded to the increasing of

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Fig. 5. Random forest analysis aiming to identify the best individual predictors of the diversity and community composition of fungi in tree plantation and natural restoration. Predictors include soil traits, litter traits, plant traits and root traits (Table 2). MSE, mean square error. A, C represent soil fungal diversity; B, D means soil fungal community composition.

soil fungal diversity (Kerfeld et al. 2018; Põlme et al. 2018; Poole et al. 2018). Most importantly, we used SEM and the pathway effects to test the combined impacts of plant and soil traits on soil fungal community due to tree plantation (priori model in Supplementary Fig. 3). We found that tree plantation directly affected soil fungal diversity by root traits, while natural restoration directly affected soil fungal diversity by soil traits (Fig. 6). In addition to tree plantation: it can be directly affected by root traits because of the large root system, while natural restoration can be directly affected by soil traits due to the higher soil nutrients. In addition, the total effects of soil traits, plant traits, litter traits and root traits were higher than natural restoration. Compared with natural restoration, tree plantation was more beneficial to soil fungal diversity, mainly due to the utilization of labile C by soil fungal community. For example, tree plantation can maintain soil water retention and improve soil physical properties and plant traits, such as the higher plant height, litter traits and root traits, which possessed a competitive advantage in terms of nutrient absorption (Laforestlapointe et al., 2017; Geml and Wagner, 2018). 4.3. Plant and soil traits driving soil fungal community due to tree plantation This study focused on plant and soil traits in explaining soil fungal community following vegetation restoration on the Loess Plateau, despite of the lower level of soil fungal diversity. However, the processes and mechanisms are still poorly unknown. Therefore, an interesting question addressed: are tree plantation and natural

restoration adaptive responses to soil fungal community on the Loess Plateau? If they are adaptive, what are the interactions and ecological mechanisms in the progresses? Here, we summarized and presented a conceptual framework, which try to disentangle this question based on our findings (Fig. 7). There were two main mechanisms: (1) the direct effects induced by root-associated organisms due to vegetation restoration and (2) the indirect effects induced by soil microbes, which promoted to the nutrient absorption (Fitzpatrick et al., 2018). For example, some nitrogen-fixing bacteria in tree plantation (mainly dominated by legumes) are benefit to soil nutrient absorption, which convert atmospheric N into ammonium-N (Wardle et al., 2004; Põlme et al., 2018; Mommer et al., 2018). In doing so, soil fungi help to breakdown the insoluble organic matter and convert to available forms for plant absorption following vegetation restoration (Belin et al., 2018; Poole et al., 2018). The other mechanism is direct effects induced by root-associated organisms, and plant roots produce large amount of organic acids, promoting nutrient cycling and microbial metabolic activities (Wardle et al., 2004; Mommer et al., 2018; Põlme et al., 2018). Compared with natural restoration, tree plantation had the more complexity of root system, litter quantity, nutrients inputs, and networks. In addition, we found that soil traits, plant traits, litter traits and root traits of tree plantation can explain more variation (87.12%) to soil fungal community than natural restoration, supporting that tree plantation had a large effect on soil fungal community than natural restoration. Recently studies indicated that soil microbial community were indirectly affected by the trophic interactions (Wardle et al., 2004). In this study, tree plantation had a large net primary

Please cite this article as: Y. Yang, H. Cheng, Y. Dou et al., Plant and soil traits driving soil fungal community due to tree plantation on the Loess Plateau, Science of the Total Environment, https://doi.org/10.1016/j.scitotenv.2019.134560

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Fig. 6. Structural equation model (SEM) showing the direct and indirect effects of tree plantation and natural restoration on soil fungal diversity. Numbers adjacent to arrows are indicative of the effect size of the relationship. Soil traits, litter traits, plant traits and root traits are included in our models as independent observable variables, thereafter, we group them in the same box in the model for graphical simplicity. Besides, SEM considered all plausible pathways, and larger path coefficients are shown as wider arrows, and blue and red colors indicate positive and negative effects, respectively. Arrow width is strongly related to the path coefficients. Path coefficients and coefficients of determination (R2) were calculated after 999 bootstraps, and represent the proportion of variance explained for each dependent variable in SEM. RMSEA, root-mean-square error of approximation. Significance levels are indicated by * (p < 0.05), ** (p < 0.01), and *** (p < 0.001). In addition, standardized direct and indirect mean effects derived from the partial least-squares path models are presented in below. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Table 1 Topological properties of co-occurring fungal community networks obtained in terms of tree plantation. Network metrics

Tree plantation

Natural restoration

Number of nodes Number of edges Number of positive correlations Number of negative correlations Average connectivity (avgK) Average clustering coefficient (avgCC) Average path length (APL) Network diameter Modularity (M)

96(7) 1523 1213(25) 1078(12) 20.47 0.597 3.65 12.9 0.523

83(5) 1104 856(15) 735(9) 13.26 0.526 3.12 13.2 0.459

and Schlechter, 2018; Zhalnina et al., 2018), resulting in the more complexity networks of soil fungal community. 5. Conclusions

productivity (NPP) and tended to support the complex food webs dominated by soil fungal community, producing a positive stability of soil fungal community (Mmm et al., 2018; Remus-Emsermann

This study provides a new insight to test the combined impacts of plant and soil traits on soil fungal community due to tree plantation on the Loess Plateau. The findings demonstrated a disproportionate influence of tree plantation on soil fungal community, and soil fungal community was mediated by soil traits, plant traits, litter traits and root traits. Specifically, soil fungal diversity was mainly controlled by SOC, soil pH, C/N in soil, biomass in litters and roots in terms of tree plantation. Interestingly, litter traits and root traits explained more variation of soil fungal community in tree plantation. Finally, we build a conceptual framework to disentangle the combined impacts of plant and soil traits on soil fungal community due to tree plantation. With the special significance

Table 2 p-values associated to the relative contribution of the different predictors used to model the richness and community composition of soil fungi in terms of tree plantation. Item Tree plantation Natural restoration

Fungal Fungal Fungal Fungal

richness composition richness composition

Soil traits

Plant traits

Litter traits

Root traits

0.0056 <0.001 <0.001 <0.001

0.0023 <0.001 0.089 <0.001

<0.001 <0.001 0.093 <0.001

<0.001 <0.001 <0.001 <0.001

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Table 3 Best-fitting model predicting the distribution of soil fungal diversity and composition in terms of tree plantation. Model A includes all parameters in Supplementary Table 1. Model B includes all parameters in model A except plant traits. Model C includes all parameters in model A except plant community-level traits. Model D includes all parameters in model A except plant traits and community-level traits. Models are ranked by Akaike information criterion (AIC). AIC measures the relative goodness of fit of a given model; the lower its value, the more likely the model is to be correct. DAIC is the difference between the AIC of each model and that of the best model. Item Tree plantation

Fungal richness

Fungal composition

Natural restoration

Fungal richness

Fungal composition

Model

Soil traits

Plant traits

Litter traits

Root traits

p value

R2

AIC

DAIC

A B C D A B C D A B C D A B C D

pH + C/N pH + C/N pH + SOC pH + SOC + C/N C/N pH + C/N pH + SOC + C/N pH + SOC + C/N pH C/N pH + SOC pH + SOC + C/N pH pH + C/N SOC + C/N pH + SOC + C/N

Height LCC

LB LB + LC LB + LC + LN LB + LN LB + LC LB + LC LB + LC LB + LN LB LB + LC LB + LC + LN LB LB + LC LB + LC + LN LB + LN LB + LC

RB + RC RB + RC + RN RB + RN RB RB RB + RC RB RB + RN RB RB + RN RB + RC RB RB + RN RB RB + RC + RN RB + RC

<0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001

0.263 0.258 0.261 0.274 0.326 0.312 0.316 0.338 0.231 0.215 0.228 0.244 0.302 0.298 0.287 0.311

423.65 463.25 487.52 493.13 659.89 632.01 625.78 667.82 326.03 321.45 322.54 327.98 358.92 351.23 349.77 362.54

1.36 2.59 3.21 3.57 2.35 2.69 3.54 4.12 0.12 0.09 0.00 0.13 0.16 0.18 0.63 0.61

Height LCC Height + LCC LCC + LNC Height + LCC Height + LCC

Fig. 7. Conceptual framework bridging knowledge on plant, soil traits and soil fungi (network) derived from tree plantation and natural restoration, illustrating plant and soil traits underlying the disparate patterns of this network. In tree plantation, there was high plant traits (such as plant height, litter quantity, root system), which show a variety of growth and nutrient acquisition strategies, and soil food web is taxonomically and functionally diverse and encompasses complex trophic relationships, while soil food webs in natural restoration are less diverse and often dominated by root herbivores, fungi and fast-growing bacteria and their consumers. Finally, a Venn diagram of variation partitioning modeling aiming to identify the relative contribution of soil traits, litter traits, plant traits and root traits to soil fungal diversity. Shared effects of these variable groups are indicated by the overlap of circles. The Venn diagram led to the following fractions: pure effect of soil traits, litter traits, plant traits and root traits. Note: The path coefficients and explained variability were calculated after 999 bootstraps. Models with different structures were assessed using the goodness-of-fit statistic, which is a measure of the overall prediction performance.

Please cite this article as: Y. Yang, H. Cheng, Y. Dou et al., Plant and soil traits driving soil fungal community due to tree plantation on the Loess Plateau, Science of the Total Environment, https://doi.org/10.1016/j.scitotenv.2019.134560

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Please cite this article as: Y. Yang, H. Cheng, Y. Dou et al., Plant and soil traits driving soil fungal community due to tree plantation on the Loess Plateau, Science of the Total Environment, https://doi.org/10.1016/j.scitotenv.2019.134560