Land use influences arbuscular mycorrhizal fungal ...

Download PDF
4 downloads 2684 Views 1MB Size Report
Comment
Soil particle size distribution (PSD) was characterized by the volume fractal ... subsample was used as a template for the second PCR amplifica- tion under the ...... r-project.org/doc/manuals/R-exts.html/ [accessed 11 July 2013]. Reinhart KO ...
Research

Land use influences arbuscular mycorrhizal fungal communities in the farming–pastoral ecotone of northern China Dan Xiang1,4, Erik Verbruggen2,3, Yajun Hu1, Stavros D. Veresoglou2,3, Matthias C. Rillig2,3, Wenping Zhou1, Tianle Xu1, Huan Li4, Zhipeng Hao1, Yongliang Chen1 and Baodong Chen1 1

State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China; 2Dahlem Center of Plant Sciences, Plant

Ecology, Freie Universit€at Berlin-Institut f€ur Biologie, Berlin, Germany; 3Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany; 4College of Resources and Environment, Qingdao Agricultural University, Qingdao, China

Summary Author for correspondence: Baodong Chen Tel: +86 10 62849068 Email: [email protected] Received: 21 April 2014 Accepted: 30 June 2014

New Phytologist (2014) doi: 10.1111/nph.12961

Key words: 454 pyrosequencing, arbuscular mycorrhizal fungi (AMF), biodiversity, driving factor, land use, the farming–pastoral ecotone of northern China.

 We performed a landscape-scale investigation to compare the arbuscular mycorrhizal fungal (AMF) communities between grasslands and farmlands in the farming–pastoral ecotone of northern China.  AMF richness and community composition were examined with 454 pyrosequencing. Structural equation modelling (SEM) and multivariate analyses were applied to disentangle the direct and indirect effects (mediated by multiple environmental factors) of land use on AMF.  Land use conversion from grassland to farmland significantly reduced AMF richness and extraradical hyphal length density, and these land use types also differed significantly in AMF community composition. SEM showed that the effects of land use on AMF richness and hyphal length density in soil were primarily mediated by available phosphorus and soil structural quality. Soil texture was the strongest predictor of AMF community composition. Soil carbon, nitrogen and soil pH were also significantly correlated with AMF community composition, indicating that these abiotic variables could be responsible for some of the community composition differences among sites.  Our study shows that land use has a partly predictable effect on AMF communities across this ecologically relevant area of China, and indicates that high soil phosphorus concentrations and poor soil structure are particularly detrimental to AMF in this fragile ecosystem.

Introduction It is well known that above- and below-ground components of ecosystems tightly interact to influence ecosystem processes and properties such as productivity and soil stability (Wardle et al., 2004). However, our understanding of below-ground community ecology remains relatively limited, in part because of methodological constraints (Fitter, 2005; Fierer et al., 2009). This is the case with arbuscular mycorrhizal fungi (AMF), which are mutualistic fungi that engage in symbiosis with the majority of vascular plants (Smith & Read, 2008; Brundrett, 2009). They are known to improve plant productivity through increased nitrogen (N) and phosphorus (P) acquisition (Hill et al., 2010), enhanced drought tolerance (Li et al., 2013) and protection from soil pathogens (Zhang et al., 2009). Not only do AMF play key roles in determining the relative performance of host plants (van der Heijden et al., 1998; Wagg et al., 2011) but they also enhance the sustainability of ecosystems by improving soil structure (Rillig & Mummey, 2006; Wilson et al., 2009). Most of these ecosystem services can be very important for ecosystems prone to degradation, and therefore more information is needed on the behaviour of these fungi in different environments. Ó 2014 The Authors New Phytologist Ó 2014 New Phytologist Trust

The farming–pastoral ecotone of northern China is one of eight critically fragile ecosystems in China. It suffers from both global climatic changes and human activities, such as increasing drought, sandstorms, overgrazing and unsustainable arable farming, and has become the most severely degraded zone in China (Liu & Gao, 2002; He et al., 2011). Until now, there have only been few studies of AMF diversity in farming–pastoral zones in China and these studies were limited to AMF associated with a few specific plant species in a relatively small region, which hampers any generalization (Qian & He, 2009; Chen et al., 2012). Therefore, although the conversion from grassland to intensive farming has been documented as a main cause of grassland degradation in this area (Hao & Ren, 2009; Ye & Fang, 2012), the impact of land use change on AMF community composition and diversity remains largely unknown. In general, an increase of land use intensity has been shown to correlate with a decrease in AMF species richness and diversity (K€onig et al., 2010; Verbruggen et al., 2010; Schnoor et al., 2011). However, there is an increasing number of reports that failed to detect strong differences in AMF communities on cropped vs noncropping systems (St€ urmer & Siqueira, 2011; Gonzalez-Cortes et al., 2012; Jefwa et al., 2012; Dai et al., 2013; New Phytologist (2014) 1 www.newphytologist.com

New Phytologist

2 Research

Jansa et al., 2014). These observations call into question the factors that might determine whether or not conversion to farmland radically changes AMF communities, and whether the answer depends on the census scale, methodology or typical degree of land use intensity (Lekberg et al., 2012; Dai et al., 2013). Potential drivers of AMF diversity and community composition changes are the identity of the host plants (Liu et al., 2012) and environmental factors such as soil pH (An et al., 2008; Dumbrell et al., 2010), soil organic carbon (Bai et al., 2009), soil phosphate (Verbruggen et al., 2012), soil nitrogen concentrations (Fitzsimons et al., 2008), and soil texture (Lekberg et al., 2007). Apart from these specific environmental factors, direct land use-related circumstances such as land-use intensity and tillage have also been suggested as primary determinants of AMF community composition (Helgason et al., 1998; Oehl et al., 2010; Schnoor et al., 2011). Land use conversion and farming practices may impact the occurrence of AMF in multiple ways, that is, changing above-ground vegetation, altering soil properties and increasing soil disturbance. Although these relationships have been established, none of these studies has thus far attempted to simultaneously quantify the relative contribution of each of these factors that are related to the land use change on AMF diversity. This inevitably hampers our ability to mitigate unsustainable anthropogenic influences on this fragile ecosystem. Here, we studied 50 paired samples from grassland and farmland covering a large area in northern China to address the following questions. Does land use practice (grassland vs farmland) influence the AMF diversity and community composition in this area? What are the biotic and abiotic predictors associated with land use that best explain this influence? By addressing these questions, we intend to provide a novel insight into drivers of AMF biodiversity in this fragile region and to contribute to the

understanding of the ecological impact of land use conversion from grassland to farmland.

Materials and Methods Study area and soil sampling The farming–pastoral ecotone of northern China is an ecological transition zone that connects the grassland livestock region and farming areas. This area is located between 35 and 50°N latitude and 100° and 125°E longitude in the arid and semiarid northern steppe with an average annual precipitation range between 300 and 450 mm and an average annual temperature range between 0 and 10°C. The total area is c. 690 000 km2 and is shared by eight provinces: Inner Mongolia, Jilin, Hebei, Shanxi, Ningxia, Qinghai, Gansu and Liaoning (Wang et al., 1999). Temporal and spatial variability of climate is one of the most notable features of this ‘transitional region’, especially for precipitation and temperature (Huang et al., 2007) which are the main drivers of plant diversity and community composition (Klein et al., 2004). We thus considered these two factors in choosing our sampling sites as follows: after data of mean annual temperature and precipitation (40 yr from 1969 to 2009) were obtained from the China Meteorological Data Sharing Service System (http://cdc.cma.gov. cn/), temperature and precipitation map were generated using ordinary Kriging interpolation based on the 40 yr average records (Supporting Information Figs S1, S2). Then temperature and precipitation intensity in the two maps were classified into five grades from low to high (Figs S3, S4). The two classified maps were then overlaid to create a superposition map with 23 regions within which sites have similar hydrothermal properties (Fig. 1). These analyses were carried out in ArcGIS 9.3 (ESRI, Redlands, CA, USA). We subsequently designated 50 sites with a balanced distribution across the hydrothermal gradient while

Fig. 1 Sampling sites in the farming– pastoral ecotone of northern China. Different colours represent different climatically uniform areas. For specific climatic conditions, see Table S1. New Phytologist (2014) www.newphytologist.com

Ó 2014 The Authors New Phytologist Ó 2014 New Phytologist Trust

New Phytologist simultaneously taking the highway system into account out of practical considerations. When visiting the sites, we made slight modifications to designated sites depending on current land use practices. Eventual sampling sites are presented in Fig. 1 (coordinates and climatic variables for each site are shown in Table S1). All soils were sampled in 2010 in August when grassland plant biomass peaks in this region. To the best of our knowledge, none of the sampled grassland sites had been ever tilled and all were dominated at the time of sampling by native grass prairie plant species. The sampling sites within grasslands were selected so that their vegetational composition resembled that of the larger local area to be as representative of regional native grassland as possible. All farmlands cropped maize (Zea mays L.) and were within a distance of 2 km of the sampled grasslands. In the studied area, farmland soil is generally tilled before cropping and maize is rotated with sorghum, millet or wheat and cropped from spring to autumn; because of temperature and precipitation restrictions, farmers generally don’t plant any crop in winter. Nitrogen and phosphorus are the main fertilizers used in this area, but specific fertilizer-use information on a farm-level basis was not available. Most common plant species in the grassland communities were Leymus chinensis (Trin. ex Bunge) Tzvelev, Setaria viridis (Linn.) Beauv., Tribulus terrestris L., Cenchrus incertus M. A. Curtis, Lespedeza davurica (Laxm.) Schindl., Artemisia capillaris Thunb., Carex humilis Leyss., Carex onoei Franch. et Savat., Chloris virgata Sw., Heteropappus altaicus (Willd.) Novopokr., Artemisia frigida Willd., Cleistogenes squarrosa (Trin.) Keng and Stipa caucasica subsp. glareosa (P. A. Smirn.) Tzvelev. At each grassland sampling site, five 1 9 1 m2 (quadrats) within an area of 100 m2 were designated for a vegetation survey. Total plant cover was estimated visually for each quadrat. All the plant species present in these five quadrats were identified. The total number of individuals per plant species in each quadrat was counted. The average height (in cm) of each plant species in each quadrat was additionally measured with a ruler. We chose the Shannon–Weiner (H 0 ) diversity index to be the plant diversity index in this paper and it was calculated for each sample site using the following equation, H 0 = Σpi (loge pi), pi = ni/N, where ni is the number of individuals of species i, and N is the total number of individuals in all species. After that, 15 soil cores (3 cm in diameter and 15 cm in depth) were sampled evenly within these five quadrats and subsequently pooled to provide a single representative sample per site. Pooling at this stage was done out of logistic and economic considerations. However, this procedure does increase the measurement error as compared with analysing soil cores separately. Given that the main interest of this paper is on between-site comparisons and the high number of sampled pairs (50), statistical power should be sufficient to distinguish real effects from noise. In farmlands, the exact same procedure was followed but no vegetation survey was performed. Soil samples were stored in polyethylene bags at 4°C in a refrigerated box. After transport to the laboratory, the composite soil samples were passed through a 2 mm sieve, homogenized, and divided into two parts. One part was kept at 80°C for molecular analysis and the other was air-dried for analysis of soil physicochemical properties and extraradical AM Ó 2014 The Authors New Phytologist Ó 2014 New Phytologist Trust

Research 3

hyphae. The mixed roots were manually collected from the surface of the 2.0 mm sieve. Also, tweezers were used to additionally collect very fine roots and these were mixed with the other roots. Soil physical and chemical properties Soil pH was determined with a soil : water ratio of 1 : 2.5 (w/v). Soil organic carbon (SOC) was determined by Walkley (1947)’s K2Cr2O7 oxidation method. Soil available phosphorus (AP) was extracted with 0.5 M NaHCO3 and measured using the Mo-Sb anti-spectrophotometry Method (Olsen et al., 1954). Soil available nitrogen (AN) was assayed through the alkaline hydrolysis method (Cornfield, 1960). Total N and total C were measured by direct combustion using a C/N analyser (Vario EL III, Hanau, Germany), and soil C : N was calculated based on total C and total N. Soil particle size was analysed using a laser diffraction technique with a Longbench Mastersizer 2000 (Malvern Instruments, Malvern, UK). Before the analysis, soil samples were pretreated with H2O2 (30%, w/w) at 72°C to decompose the organic matter. Soil aggregates were then dispersed by adding sodium hexametaphosphate and sonicating the samples for 30 s (Hu et al., 2013). The soil particle size was partitioned into clay (0–2 lm), silt (2–50 lm) and sand (50–2000 lm) according to the classification system of the US Department of Agriculture (USDA). The soil particle sizes from 0 to 2000 lm were divided into 64 classes using the software package of the laser particle analyser. Soil particle size distribution (PSD) was characterized by the volume fractal dimension value (D value) in order to control the number of variables used in structural equation modelling (Hu et al., 2013). Measurements of all soil physicochemical parameters were performed in triplicate so as to minimize the experimental errors. Measures of soil physicochemical parameters, AMF colonization and hyphal length density for grasslands only have previously been published in Hu et al. (2013). Microscopic and molecular identification of AM fungi Root samples were washed carefully with tap water and cut into segments of c. 1 cm length. After thorough mixing to homogenize plant roots, a random subsample of these roots (> 100 pieces) was used for staining. Root segments were cleared in 10% (w/v) KOH at 90°C and stained with 0.05% Trypan Blue in lactoglycerol, and then stored in lactoglycerol (McGonigle et al., 1990). Thirty randomly selected 1 cm root segments were examined for AM intensity of colonization (M%) at 9200 magnification according to Trouvelot et al. (1986). Extraradical hyphae were extracted from a 4 g soil subsample by the membrane filter technique modified after Jakobsen et al. (1992). In brief, duplicate 4 g soil samples were mixed with and suspended in 250 ml deionized water, and hyphae in 5 ml aliquots were collected on 25 mm membrane filters (1.2 mm pore size) and stained with Trypan Blue (Hu et al., 2013). Then the hyphal length density (HLD) was measured by the gridline intercept method at 9200 magnification New Phytologist (2014) www.newphytologist.com

New Phytologist

4 Research

(Tennant, 1975). The hyphal length of each soil sample was measured on six separate membranes. For DNA isolation, the soils were first shaken in the ziplock bags for several seconds in order to mix them further. Then some soil (0.5–1 g) was spread out on a piece of weighing paper (c. 12 9 12 cm2) and all the visible roots whose length was bigger than the size of soil particles (2 mm) were removed using tweezers. Afterwards, DNA was isolated from 500 mg of soil using the FastDNA Spin kit for soil (MP Biomedicals, Vista, CA, USA) according to the manufacturer’s recommendations. The extracted DNA was diluted with double-distilled H2O to 10–20 ng ll1 and subjected to a nested PCR with AML1/ AML2 (Lee et al., 2008) and NS31/AM1 (Simon et al., 1992; Helgason et al., 1998). The latter primer pair was augmented with the 454 pyrosequencing adapters and 7-bp-long barcodes for multiplexing, resulting in the following constructs: 50 - CGTATCGCCTCCCTCGCGCCATCAG (NNNNNNN) TTGGAGGGCAAGTCTGGTGCC -30 ; and 50 - CTATGCGCC TTGCCAGCCCGCTCAG GTTTCCCGTAAGGCGCCGAA-30 (A and B adapters are underlined, the barcode is indicated by Ns in parentheses and specific primers NS31 and AM1 are shown in italics). The first PCR (25 ll volume) contained 19 PCR buffer, 1.5 mM MgCl2, 0.2 mM dNTPs, 0.5 mM of each primer, 1.5 U Ex Taq polymerase (TaKaRa, Dalian, China), 2 ll DNA template and 7.5 lg BSA with the following cycling conditions: 94°C for 3 min; 30 cycles at 94°C for 30 s, 58°C for 45 s, 72°C for 60 s; followed by 72°C for 10 min. The first amplification product was diluted with double-distilled H2O (1 : 10) and a 1 ll subsample was used as a template for the second PCR amplification under the same conditions as in the original PCR, except for the volume, which was 50 ll, the absence of BSA in the PCR reaction, the number of PCR cycles performed (35 instead of 30) and the extension time of the PCR (45 s instead of 30 s). All PCR amplifications were performed in an Eppendorf Mastercycler pro thermocycler (Eppendorf, Hamburg, Germany). PCR products were separated by gel electrophoresis (1.5% agarose in 0.5 9 TAE), bands were excised, and purified using the QIAEX II Gel Extraction Kit (Qiagen). The amount of DNA in the purified PCR products was measured using a NanoDrop 1000 (Thermo Scientific, Wilmington, DE, USA). The products were mixed at equimolar concentrations and then subjected to sequencing on a Roche 454 FLX System. Sequencing was performed by the Chinese National Human Genome Center in Shanghai. Bioinformatics The 454 pyrosequencing reads with ambiguous nucleotides, a quality score < 20, lacking a complete barcode and NS31 primer, or shorter than 160 bp (excluding the barcode and primer sequences) were removed and excluded from further analysis. Denoising of the original flowgrams was done using the Mothur implementation of PyroNoise (shhh.flows) and chimeras were detected using the ‘chimera.seqs’ command in ‘Mothur’ (Schloss et al., 2009) and removed. Identical sequences were grouped New Phytologist (2014) www.newphytologist.com

using the ‘unique.seqs’ command. Unique sequences were clustered into operational taxonomic units (OTUs) using the unsupervised Bayesian clustering algorithm CROP (Hao et al., 2011) with a 97% identity threshold. Clusters with fewer than five reads were removed to reduce the risk of artificially inflating richness as a result of sequencing error. The longest sequence from each OTU was selected as the representative sequence (the minimum length of representative sequences was 467 bp), and a manual blasting against the GenBank nonredundant nucleotide database was used to detect nonGlomeromycota sequences. The BLASTbased OTU identities were further confirmed by phylogenetic analysis incorporating reference sequences from both GenBank and MaarjAM databases in March 2014, so as to confirm OTU identity at the genus level (Fig. S5). The closest sequence of a matching VT was then added to the phylogenetic tree. Representative sequences from each encountered AM fungal OTU have been deposited in GenBank (accession numbers KJ659082– KJ659182). Statistical analyses To compare a-diversity between samples, we calculated various indexes (Sobs, Chao, Invsimpson, Shannon) after resampling the read number for each sample to 500 reads in Mothur (version 1.23.1) (http://www.mothur.org) (for rarefaction curves of Shannon diversity, see Fig. S6).We excluded one sample from farmland that had fewer than 500 sequence reads, and compared AM fungal diversity and richness between grasslands and farmlands using 49 paired samples. Data that were not normally distributed were log(x + 1)-transformed. Raw community composition data for AM fungi were square-root-transformed in order to down-weight the importance of dominant taxa. The comparison between farmlands and grasslands regarding a -diversity indices of AMF, root colonization, HLD and soil characteristics were tested using paired t-tests with the software package SPSS 18.0 (SPSS Inc., Chicago, IL, USA). All AMF community-related analyses were based on relative abundances of OTUs per sample, and the Bray–Curtis index was used as a distance measure unless otherwise stated. Principal coordinate analysis (PCoA) of AMF communities was conducted using the CANOCO 5.0 software (Microcomputer Power, Ithaca, NY, USA). To establish whether there were significant AMF community composition differences between grasslands and farmlands, PERMANOVA was carried out with the vegan package (Oksanen et al., 2012) in R version 2.15.2 (R Development Core Team, 2012). In addition, to test whether the AMF community compositions were phylogenetically clustered within land use types, field pairwise phylogenetic distances were calculated using mean nearest taxon distance (MNTD) based on AMF relative abundance using the picante package in R (Kembel et al., 2010), and then a PERMANOVA was performed to test whether there was a phylogenetic pattern between land use types. Indicator species analysis was performed to test whether OTUs were more abundant in either farmlands or grasslands using the ‘signassoc’ function in the ‘indicspecies’ package in R (De Caceres & Legendre, 2009). We used mode = 0 (site-based) and report Ó 2014 The Authors New Phytologist Ó 2014 New Phytologist Trust

New Phytologist

Research 5

P-values after Sidak’s correction for multiple testing. A heatmap illustrating the relative abundance of AMF indicator OTUs was generated using the gplots package (Warnes et al., 2011) in R. The relationships between AMF communities and environmental factors were assessed using Mantel tests with the ecodist package (Goslee & Urban, 2007) in R. All environmental factors were standardized (range 0–1) and Euclidean distances between sites were calculated for the Mantel test. Because a total of 14 environmental factors were tested, a Bonferroni correction was implemented to test for significance in that effects were considered significant only if P-values were lower than 0.0036 (0.05/14). To control for covarying effects of factors, additional partial Mantel tests were carried out with each of the significant independent variables according to the result of Mantel test: for each of the models, distances between sites based on all environmental variables, except the one used as an independent variable, were incorporated in the form of a conditional variable. To estimate the direct and indirect effects of land use on AMF richness and HLD, structural equation models were fitted to our data using the software LISREL8.72 (J€oreskog & Sorbom, 2001). As explanatory variables we used AP, PSD and ‘soil fertility index’; the latter is a synthetic variable derived from the first axis of the PCA of SOC, AN, soil total C and soil total N, with 97.4, 95.6, 92.4 and 74.7% factor loadings, respectively, and with 88.6% total explainable variance. Soil AP was used as a separate indicator because of its proposed close relationship with AMF community dynamics (Liu et al., 2012). We tested whether the model has a significant model fit according to the following criteria: v2/df < 2, P-values (P > 0.05), root mean square error of approximation (RMSEA) < 0.07 and goodness of fit index (GFI) > 0.9 (Hooper et al., 2008).

Compared with farmlands, AMF Invsimpson, Shannon index, number of AMF OTUs (Sobs) and number of estimated asymptotic AMF taxon richness (Chao index) were significantly higher in grasslands (Table 1). HLD was also significantly higher in grassland, but mycorrhizal root colonization was significantly lower in grassland than in farmland (Table 2). Principal coordinate analysis based on relative abundance of OTUs showed differences in AMF community composition between grasslands and farmlands (Fig. 2). This observation was confirmed by PERMANOVA (r = 0.3007, P = 0.001). In addition, there was a significant phylogenetic pattern between these two land use types (r = 0.3358, P = 0.001), which indicates that changes in AMF community composition were phylogenetically nonrandom. The 11 AMF genera covered by OTUs were: Glomus, Funneliformis, Rhizophagus, Claroideoglomus, Acaulospora, Ambispora, Archaeospora, Diversispora, Gigaspora, Paraglomus and Septoglomus. There were some differences in the dominant genera between grasslands and farmlands: in grasslands, Glomus was on average more represented, whereas in farmlands Septoglomus had higher representation, approaching that of Glomus (Fig. 3). Out of the total of 101 OTUs, 30 OTUs of AMF were found to be significant indicator species of grasslands or farmlands. Of these, 19 were more abundant in grasslands than in farmlands and 11 OTUs were significantly more abundant in farmlands (Fig. 4).

Results

Relationships between AMF parameters and soil properties

Overall pyrosequencing information A total of 231 240 sequences (ranging from 599–15 819 reads per sample) with a length ≥ 160 bp (maximum 534 bp, median 386 bp) were obtained from the grasslands and 246 396 sequences (1026–13 972 reads per sample; maximum 534 bp, median 390 bp) from the farmlands. The total number of Glomeromycota sequences among the 454 sequencing reads obtained from grasslands was 221 112 (95.62% of all reads, 59.62–100% of reads per sample) according to BLAST results using the nonredundant GenBank database. These sequences could be assigned to 90 OTUs (belonging to 11 genera). For farmlands, a total

number of 210 183 Glomeromycota sequences (85.30% of all sequences, 23.46–99.81% of sequences of each sample) could be assigned to 83 OTUs (belonging to 11 genera; Table S2). AMF a-diversity, colonization and community composition

Soil organic carbon, AN, total C, total N, and clay and sand content were all significantly higher in grasslands than in farmlands, while the opposite (significantly higher values for farmlands) was recorded for AP, moisture content and silt content. There were no differences in soil pH between the two land use types (Table 2). We used SEM to assess the extent of direct and indirect effects of land use on AMF richness and HLD (Fig. 5). The fitted models met our significance criteria (v2 = 2.74, df = 2, P = 0.254, RMSEA = 0.062, GFI = 0.99; and v2 = 2.78, df = 2, P = 0.249, RMSEA = 0.063, GFI = 0.99, respectively) and accounted for 28 and 35% of the variation in AMF diversity and HLD. Land use showed no significant direct effect on either

Table 1 a-diversity of arbuscular mycorrhizal (AM) fungi in the grasslands and farmlands of the farming–pastoral ecotone of northern China Land use

Sobs (richness)

Grassland Farmland

20.71  1.12** 17.71  0.83

1

Chao

Invsimpson

Shannon

23.68  1.36** 19.72  0.97

5.79  0.36** 4.82  0.28

2.03  0.07** 1.86  0.07

The a-diversity index is presented as the number of detected operational taxonomic units (OTUs) (Sobs); the number of estimated asymptotic AM fungal taxon richness (Chao index); and the Invsimpson and Shannon indexes. Data are means  SE (n = 49). 1 a-diversity indices were compared between farmland and grassland by paired t-test: **, P < 0.05. Ó 2014 The Authors New Phytologist Ó 2014 New Phytologist Trust

New Phytologist (2014) www.newphytologist.com

New Phytologist

6 Research Table 2 Hyphal length density and root colonization of arbuscular mycorrhizal fungi (AMF), and physicochemical properties of grassland and farmland soils in the farming–pastoral ecotone of northern China Land use type Variable

Grassland

Farmland

Hyphal length density (g m1) Root colonization (%) pH SOC (g kg1) AN (mg kg1) AP (mg kg1) Total carbon (C) (%) Total nitrogen (N) (%) Moisture (%) Clay (%) Silt (%) Sand (%)

3.53  0.28**1 30.76  2.33 8.08  0.10 13.24  1.63*** 70.71  8.19** 3.27  0.32 2.29  0.18*** 0.139  0.01** 2.99  0.31 3.68  0.25*** 46.01  2.64 50.31  2.87**

2.65  0.21 51.24  2.17*** 7.94  0.13 9.24  0.99 57.98  6.34 12.57  1.34*** 1.74  0.12 0.099  0.01 3.89  0.40* 1.16  0.07 53.34  2.54*** 45.49  2.59

Data are means  SE (n = 50). All parameters were compared between grassland and farmland by paired t-test: ***, P < 0.001; **, P < 0.01; *, P < 0.05. SOC, soil organic carbon; AN, available nitrogen; AP, available phosphorus. 1

Fig. 2 Principal coordinate analysis (PCoA) of arbuscular mycorrhizal fungi (AMF) communities in the grasslands (open circles) and farmlands (closed triangles). The community metrics were calculated from relative abundances of AMF operational taxonomic units (OTUs).

AMF richness or HLD, but effects were mediated through the measured environmental predictors: soil fertility index, soil structure (PSD) and AP contributed significantly to AMF diversity, while soil structure (PSD) and AP contributed significantly to HLD (Fig. 5). Soil AP was the most important driving factor on diversity and HLD, with the highest total path coefficients of 0.41 and 0.68 for richness and HLD, respectively (Fig. 5). A similar relationship between AMF richness, HLD and the environmental factors was also observed using correlation analysis and partial correlation analysis, where land use and plant diversity or cover were found not to be significantly correlated with either measure (Tables S3, S4). This is further supported by the absence of a correlation between plant and AMF diversity or HLD (Table S5) when analysing grasslands only. New Phytologist (2014) www.newphytologist.com

When comparing the effect of these environmental predictors on AMF community composition, it was found that land use still had no effect on AMF community composition when controlling for other predictors. Soil silt proportion (%) showed the strongest effect, while soil total C (%), SOC, pH and soil total N (%) also showed significant influences on AMF community composition (Table 3).

Discussion The study presented here is one of the largest scale comparisons of AMF communities between farmlands and grasslands so far in the literature, focusing on the environmentally relevant farming– pastoral ecotone of northern China. It was found that AMF community composition was clearly different between these two land use practices, and that AMF diversity, richness and HLD were significantly lower in farmlands than in grasslands. This is in accordance with other studies that report a negative correlation between land use intensity and AMF biodiversity in other parts of the world (Alguacil et al., 2008; K€onig et al., 2010; Oehl et al., 2010; Schnoor et al., 2011). By contrast, there are also studies that show unchanged species diversity and richness despite dramatic land use changes (St€ urmer & Siqueira, 2011; GonzalezCortes et al., 2012; Jefwa et al., 2012; Dai et al., 2013). For instance, Dai et al. (2013) sampled agricultural fields and remnants of prairie grassland in eastern Canada using next-generation sequencing. They found that croplands contained, on average, the same diversity of AMF as grasslands. Nevertheless, these grasslands only accounted for a small fraction of the landscape which was dominated by cropland, while in our study grasslands were the dominant landscape features, which may thus partly explain the different findings. Moreover, methodological differences could have been an additional cause of the differences € in findings among different studies. For example, Opik et al. (2009) found that the number of OTUs obtained from pyrosequencing was 33% higher than those found by Sanger sequencing using the same PCR protocol and primers. Therefore, the 454 pyrosequencing methodology employed here is likely to provide a more accurate survey of AMF biodiversity than other approaches. Thus, our data strongly suggest there is a negative effect of land use change on AMF biodiversity in this fragile farming–pastoral ecotone, but such effects may be dependent on region-specific factors such as typical land use intensity (Jansa et al., 2014). In contrast to richness and HLD, the AMF colonization in farmlands was significantly higher than that in grasslands. This may be have been because maize is a warm-season C4 grass, and these grasses are commonly found to have higher AMF root colonization than C3 grasses (Alguacil et al., 2008; Reinhart et al., 2012). In the grasslands, the latter were most dominant, although C4 plants were also relatively common. For example, one C4 plant species (Cleistogenes squarrosa) has more than 10% relative abundance in 14 of the grassland sites. Grasslands and farmlands differ in a range of properties, such as plant cover, soil physicochemical characteristics and anthropogenic pressure such as tillage and chemical applications. An Ó 2014 The Authors New Phytologist Ó 2014 New Phytologist Trust

New Phytologist

Research 7

Fig. 3 Proportion of total read numbers of operational taxonomic units (OTUs) grouped by genus of arbuscular mycorrhizal fungi (AMF) in the grasslands and farmlands. Based on paired t-tests, genera that are significantly more abundant in a particular land use type are indicated by asterisks (***, P < 0.001).

Fig. 4 Heatmap of relative abundances of arbuscular mycorrhizal fungal (AMF) indicator species that were found to differ significantly between grasslands and farmlands. The 50 columns on the left represent 50 sampling sites in the grasslands, while the 50 columns on the right represent corresponding sampling sites in the farmlands. The upper 19 operational taxonomic units (OTUs) exhibit higher relative abundance in grasslands, while the lower 11 OTUs exhibit higher relative abundance in the farmlands.

important subsequent question is which of these factors contributes most to the differences in the respective AMF communities. Earlier work showed that soil pH and organic matter content (Tchabi et al., 2008), soil P availability (Sheng et al., 2013), mechanical soil disturbance (Schnoor et al., 2011) and soil type (Oehl et al., 2010) can drive changes in AMF occurrence from natural ecosystems to crop production systems. However, each of these studies was restricted to a small regional scale and did not simultaneously consider multiple factors within a larger environmental gradient. Here, we have investigated these factors across a balanced environmental gradient using correlation analysis and SEM. We found that AMF diversity and HLD were not directly affected by land use, but were rather indirectly affected through soil AP and soil structure (PSD). This indicates that soil AP is Ó 2014 The Authors New Phytologist Ó 2014 New Phytologist Trust

likely to be one of the primary contributors to reduced AMF diversity and HLD in the farmlands in this research area. This study is not the first to reveal a negative correlation between AMF and soil P, but it enabled a quantitative estimation of the relative importance of soil AP to other potential predictors such as soil structure and fertility in determining AMF diversity. We found that, even after consideration of these other factors, soil P remained one of the predominant drivers of AMF response to land use conversion. One possible explanation for this finding is that a high P concentration in soil can reduce carbohydrate supply for AMF from plants, which may select for particular AMF taxa or lead to loss of taxa as a result of a general decline of community sizes. In contrast to generally consistent effects of soil P, N additions have been shown sometimes to cause AMF New Phytologist (2014) www.newphytologist.com

New Phytologist

8 Research

(a)

Table 3 Relationships of arbuscular mycorrhizal fungi (AMF) community structure with land use type, soil characteristics and above-ground plant coverage, as revealed by Mantel test and partial Mantel test Mantel test

(b)

Partial Mantel test

Explanatory factor

rM

P

rM

P

Silt% C% SOC pH Available nitrogen (N) Available phosphorus (P) Clay% Plant diversity Land use N% Sand% Plant coverage C/N Moisture%

0.280 0.279 0.270 0.220 0.164 0.169 0.204 0.229 0.224 0.131 0.093 0.107 0.036 0.049

0.001 0.001 0.001 0.001 0.001 0.003 0.001 0.001 0.001 0.021 0.041 0.104 0.827 0.216

0.277 0.221 0.220 0.200 0.129 0.092 0.059 0.024 0.029 – – – – –

0.001 0.001 0.001 0.001 0.009 0.054 0.119 0.672 0.774 – – – – –

Values in bold indicate significant correlations (P < 0.0036). SOC, soil organic carbon.

Fig. 5 The structural equation model showing the hypothesized causal relationships between environmental factors and arbuscular mycorrhizal (AM) fungal diversity (richness, a) and hyphal length density (b). The width of arrows indicates the strength of the standardized path coefficient (***, P < 0.001; **, P < 0.01; *, P < 0.05). The e-values represent residuals. PSD, soil particle size distribution. Soil fertility index is a synthetic variable derived from the first axis of the principal component analysis (PCA) of soil organic carbon (SOC), available nitrogen (N), soil total carbon (C) and soil total N with 97.4, 95.6, 92.4 and 74.7% factor loadings, respectively, and with 88.6% total explainable variance. RMSEA, root mean square error of approximation; GFI, goodness of fit index.

abundance and diversity to increase (Egerton-Warburton et al., 2007; Yang et al., 2011), decline (Liu et al., 2012) or remain constant (Johnson et al., 2003; Chen et al., 2014). This means that the response of AMF to N addition is highly variable, which may indicate that its effect depends on whether the system is N-limited (Johnson, 2010). In our study, soil AN and SOC had a significant positive correlation with both AMF richness and HLD, suggesting that N ameliorates P-induced AMF reduction, or that AMF perform better under higher N concentrations independent of plant-mediated effects (Hodge et al., 2001). We did not detect a significant relationship between plant diversity and AMF diversity or HLD in our study. This indicates that even systems with very low plant diversity can maintain a reasonably high AMF diversity and abundance. In a long-term New Phytologist (2014) www.newphytologist.com

field trial, Antoninka et al. (2011) even found higher AMF spore abundance, morphotype richness and HLD in monoculture than in polyculture plots. Similar to our study, Oehl et al. (2010) detected no relationship between AMF and plant species richness, but a strong influence of land use intensity and soil type on AMF diversity. These different observations indicate that future studies should take biotic and abiotic factors into account simultaneously to unravel the relationship between AMF and plant biodiversity. Besides AP, soil structure (PSD) was another important driving factor for AMF diversity and HLD. This is in accordance with the studies by Lekberg et al. (2007) and Veresoglou et al. (2012). As nutrient content, soil aeration and water-holding capacity vary with soil structure, each of these alone or in combination may cause a positive influence of PSD on AMF diversity and HLD. As argued by Hu et al. (2013), the fact that most soils in our study area are highly sandy and well aerated may indicate a limited importance of soil aeration as an environmental filter. However, SEM indicated that PSD had strong effects on soil C and N contents and might thus have indirectly affected AMF diversity. According to the SEM, farming practice in this area also showed a positive indirect effect on AMF biodiversity and HLD through higher PSD, but this effect was insufficient to overcome the negative influence of higher soil AP concentrations. This implies that reduced use of P fertilizer and preventing erosion through improving soil structure are likely to increase AMF abundance and diversity, which may then contribute to preventing further degeneration. Land use did not have a direct effect on AMF community composition when controlling for other environmental predictors, while soil silt content (%) was the strongest predictor. Besides soil structure, other soil properties like soil C%, SOC, AN and soil pH all showed significant influences on AMF community composition. This is consistent with other landscapescale studies such as Hazard et al. (2013) and Jansa et al. (2014), Ó 2014 The Authors New Phytologist Ó 2014 New Phytologist Trust

New Phytologist where soil abiotic properties were found to be the major determinants of AMF community composition while land management had no effect on AMF community. These studies indicate that effects of land management could be strongest when land management has a pronounced effect on soil properties, and thus indirectly alters AMF community composition. Indeed, in our study, we clearly demonstrate these indirect effects, because the effect of land use on AMF communities was mediated through soil properties. However, it should be noted that specific agricultural practices, such as fertilization concentrations, tillage intensity and field history, were not known in the current study, while these factors may have additional explanatory power regarding AMF abundance and community composition. Further research is still needed to uncover specific management practices that may ameliorate the impact of arable agriculture on AMF communities in this region in northern China. The changes in AMF community composition were found to be phylogenetically nonrandom, which indicates that more closely related AMF taxa are more likely to respond similarly to land use than distantly related taxa. At the genus level, more sequences were obtained belonging to Glomus in grasslands, while Funneliformis, Septoglomus and Claroideoglomus were proportionally more abundant in farmlands. A similar pattern was found when looking at the taxon level, as the indicator species for grasslands mainly belonged to Glomus, while indicators in farmland mainly belonged to both Glomus and Septoglomus. These findings do not support the notion that arable farming and associated tillage generally favour AM fungi within the Glomeraceae family (Jansa et al., 2002). Rather, taxa that perform well in farmlands are distributed across the Glomeromycota phylum, although our results show that phylogenetic patterns do exist. Moreover, the AMF species that were found to be compatible with arable agriculture correspond well to those found in other studies: Funneliformis mosseae has been reported as a common species in agricultural fields throughout the world (Rosendahl et al., 2009), and closer to the region studied here, Gai et al. (2010) reported that Claroideoglomus etunicatum and F. mosseae were the dominant AMF species in the rhizosphere of various crop plants in north and northwest China. In conclusion, the results presented here clearly demonstrate the negative impacts of land use conversion from grassland to farmland on AMF abundance and species richness in northern China. The study area covers sufficient environmental gradients and thus allowed simultaneous examination of multiple influencing factors related to land use: this indicated that differences in AMF diversity between grasslands and farmlands were primarily mediated through soil phosphate concentrations and soil structure. This study has enabled a deeper insight into the impacts of anthropogenic activities on AMF communities in the semiarid region of northern China, although future work is needed to tease out additional contributions of more specific agricultural practices that were beyond the current study. Through emphasizing the importance of appropriate soil management, this study will hopefully contribute to the effort of preventing soil degradation and maintaining ecosystem integrity in this fragile region. Ó 2014 The Authors New Phytologist Ó 2014 New Phytologist Trust

Research 9

Acknowledgements We wish to thank Mr Sebastian Horn, Mr Guangyu Zhou and Mr Xiaoxue Wang for their help with the data analysis. This research was supported by National Natural Science Foundation of China (41071178), the Knowledge Innovation Program of the Chinese Academy of Sciences (KZCX2-YW-BR-17), and a Joint Project of the Ministry of Environmental Protection, P.R. China, and the Chinese Academy of Sciences (STSN-21-04). D.X. was supported by a fellowship from the China Scholarship Council (CSC File no. 201204910330) during the writing of this paper. None of the authors have a conflict of interest.

References Alguacil M, Lumini E, Roldan A, Salinas-Garcia J, Bonfante P, Bianciotto V. 2008. The impact of tillage practices on arbuscular mycorrhizal fungal diversity in subtropical crops. Ecological Applications 18: 527–536. An GH, Miyakawa S, Kawahara A, Osaki M, Ezawa T. 2008. Community structure of arbuscular mycorrhizal fungi associated with pioneer grass species Miscanthus sinensis in acid sulfate soils: habitat segregation along pH gradients. Soil Science and Plant Nutrition 54: 517–528. Antoninka A, Reich PB, Johnson NC. 2011. Seven years of carbon dioxide enrichment, nitrogen fertilization and plant diversity influence arbuscular mycorrhizal fungi in a grassland ecosystem. New Phytologist 192: 200–214. Bai C, He XL, Tang HL, Shan BQ, Zhao LL. 2009. Spatial distribution of AMF, glomalin and soil enzymes under the canopy of Astragalus adsurgens Pall in the Mu US Sandland, China. Soil Biology and Biochemistry 41: 942–947. Brundrett MC. 2009. Mycorrhizal associations and other means of nutrition of vascular plants: understanding the global diversity of host plants by resolving conflicting information and developing reliable means of diagnosis. Plant and Soil 320: 37–77. Chen Z, He XL, Guo HJ, Yao XQ, Chen C. 2012. Diversity of arbuscular mycorrhizal fungi in the rhizosphere of three host plants in the farming– pastoral zone, north China. Symbiosis 57: 149–160. Chen YL, Zhang X, Ye JS, Han HY, Wan SQ, Chen BD. 2014. Six-year fertilization modifies the biodiversity of arbuscular mycorrhizal fungi in a temperate steppe in Inner Mongolia. Soil Biology and Biochemistry 69: 371–381. Cornfield AH. 1960. Ammonia released on treating soils with N sodium hydroxide as a possible means of predicting the nitrogen-supplying power of soils. Nature 187: 260–261. Dai M, Bainard LD, Hamel C, Gan Y, Lynch D. 2013. Impact of land use on arbuscular mycorrhizal fungal communities in rural Canada. Applied and Environmental Microbiology 79: 6719–6729. De Ca ceres M, Legendre P. 2009. Associations between species and groups of sites: indices and statistical inference. Ecology 90: 3566–3574. Dumbrell AJ, Nelson M, Helgason T, Dytham C, Fitter AH. 2010. Relative roles of niche and neutral processes in structuring a soil microbial community. The ISME Journal 4: 337–345. Egerton-Warburton LM, Johnson NC, Allen EB. 2007. Mycorrhizal community dynamics following nitrogen fertilization: a cross-site test in five grasslands. Ecological Monographs 77: 527–544. Fierer N, Strickland MS, Liptzin D, Bradford MA, Cleveland CC. 2009. Global patterns in belowground communities. Ecology Letters 12: 1238–1249. Fitter AH. 2005. Darkness visible: reflections on underground ecology. Journal of Ecology 93: 231–243. Fitzsimons MS, Miller RM, Jastrow JD. 2008. Scale-dependent niche axes of arbuscular mycorrhizal fungi. Oecologia 158: 117–127. Gai JP, Zhang JL, Feng G, Li XL. 2010. Arbuscular mycorrhizal fungi diversity and contribution to crop growth in agricultural fields. Journal of Integrated Field Science 7: 41–45. Gonza lez-Cortes J, Vega-Fraga M, Varela-Fregoso L, Martınez-Trujillo M, Carreon-Abud Y, Gavito M. 2012. Arbuscular mycorrhizal fungal (AMF) New Phytologist (2014) www.newphytologist.com

10 Research communities and land use change: the conversion of temperate forests to avocado plantations and maize fields in central Mexico. Fungal Ecology 5: 16– 23. Goslee SC, Urban DL. 2007. The ecodist package for dissimilarity-based analysis of ecological data. Journal of Statistical Software 22: 1–19. Hao X, Jiang R, Chen T. 2011. Clustering 16S rRNA for OTU prediction: a method of unsupervised Bayesian clustering. Bioinformatics 27: 611–618. Hao HM, Ren ZY. 2009. Land use/land cover change (LUCC) and eco-environment response to LUCC in farming-pastoral zone, China. Agricultural Sciences in China 8: 91–97. Hazard C, Gosling P, van der Gast CJ, Mitchell DT, Doohan FM, Bending GD. 2013. The role of local environment and geographical distance in determining community composition of arbuscular mycorrhizal fungi at the landscape scale. The ISME Journal 7: 498–508. He XL, Chen C, He B. 2011. Spatial distribution of arbuscular mycorrhizal fungi and glomalin of Hippophae rhamnoides L. in farming-pastoral zone from the two northern provinces of China. Acta Ecologica Sinica 31: 1653–1661. van der Heijden MGA, Klironomos JN, Ursic M, Moutoglis P, Streitwolf-Engel R, Boller T, Wiemken A, Sanders IR. 1998. Mycorrhizal fungal diversity determines plant biodiversity, ecosystem variability and productivity. Nature 396: 69–72. Helgason T, Daniell T, Husband R, Fitter A, Young J. 1998. Ploughing up the wood-wide web? Nature 394: 431. Hill J, Simpson R, Ryan M, Chapman D. 2010. Root hair morphology and mycorrhizal colonisation of pasture species in response to phosphorus and nitrogen nutrition. Crop and Pasture Science 61: 122–131. Hodge A, Campbell CD, Fitter AH. 2001. An arbuscular mycorrhizal fungus accelerates decomposition and acquires nitrogen directly from organic material. Nature 413: 297–299. Hooper D, Coughlan J, Mullen M. 2008. Structural equation modelling: guidelines for determining model fit. Electronic Journal of Business Research Methods 6: 53–60. Hu YJ, Rillig MC, Xiang D, Hao ZP, Chen BD. 2013. Changes of AM fungal abundance along environmental gradients in the arid and semi-arid grasslands of northern China. PLoS ONE 8: e57593. Huang D, Wang K, Wu WL. 2007. Dynamics of soil physical and chemical properties and vegetation succession characteristics during grassland desertification under sheep grazing in an agro-pastoral transition zone in Northern China. Journal of Arid Environments 70: 120–136. Jakobsen I, Abbott LK, Robson AD. 1992. External hyphae of vesicular-arbuscular mycorrhizal fungi associated with Trifolium subterraneum L. 1. Spread of hyphae and phosphorus inflow into roots. New Phytologist 120: 371–380. Jansa J, Erb A, Oberholzer HR, Smilauer P, Egli S. 2014. Soil and geography are more important determinants of indigenous arbuscular mycorrhizal communities than management practices in Swiss agricultural soils. Molecular Ecology 23: 2118–2135. Jansa J, Mozafar A, Anken T, Ruh R, Sanders I, Frossard E. 2002. Diversity and structure of AMF communities as affected by tillage in a temperate soil. Mycorrhiza 12: 225–234. Jefwa J, Okoth S, Wachira P, Karanja N, Kahindi J, Njuguini S, Ichami S, Mung’atu J, Okoth P, Huising J. 2012. Impact of land use types and farming practices on occurrence of arbuscular mycorrhizal fungi (AMF) Taita-Taveta district in Kenya. Agriculture, Ecosystems & Environment 157: 32–39. Johnson NC. 2010. Resource stoichiometry elucidates the structure and function of arbuscular mycorrhizas across scales. New Phytologist 185: 631–647. Johnson NC, Rowland DL, Corkidi L, Egerton-Warburton LM, Allen EB. 2003. Nitrogen enrichment alters mycorrhizal allocation at five mesic to semiarid grasslands. Ecology 84: 1895–1908. J€oreskog KG, Sorbom D. 2001. LISREL 8: User’s reference guide. Chicago, IL, USA: Scientific Software International. Kembel SW, Cowan PD, Helmus MR, Cornwell WK, Morlon H, Ackerly DD, Blomberg SP, Webb CO. 2010. Picante: R tools for integrating phylogenies and ecology. Bioinformatics 26: 1463–1464. Klein JA, Harte J, Zhao XQ. 2004. Experimental warming causes large and rapid species loss, dampened by simulated grazing, on the Tibetan Plateau. Ecology Letters 7: 1170–1179. New Phytologist (2014) www.newphytologist.com

New Phytologist K€onig S, Wubet T, Dormann CF, Hempel S, Renker C, Buscot F. 2010. TaqMan real-time PCR assays to assess arbuscular mycorrhizal responses to field manipulation of grassland biodiversity: effects of soil characteristics, plant species richness, and functional traits. Applied and Environmental Microbiology 76: 3765–3775. Lee J, Lee S, Young JPW. 2008. Improved PCR primers for the detection and identification of arbuscular mycorrhizal fungi. FEMS Microbiology Ecology 65: 339–349. Lekberg Y, Koide RT, Rohr JR, Aldrich-Wolfe L, Morton JB. 2007. Role of niche restrictions and dispersal in the composition of arbuscular mycorrhizal fungal communities. Journal of Ecology 95: 95–105. Lekberg Y, Schnoor T, Kjøller R, Gibbons SM, Hansen LH, Al-Soud WA, Sørensen SJ, Rosendahl S. 2012. 454-sequencing reveals stochastic local reassembly and high disturbance tolerance within arbuscular mycorrhizal fungal communities. Journal of Ecology 100: 151–160. Li T, Hu YJ, Hao ZP, Li H, Wang YS, Chen BD. 2013. First cloning and characterization of two functional aquaporin genes from an arbuscular mycorrhizal fungus Glomus intraradices. New Phytologist 197: 617–630. Liu LD, Gao YB. 2002. Eco-environmental construction and functional integration of the farmland-pastoral ecotones in the northern part of China. Advances in Earth Science 17: 174–182 (in Chinese). Liu YJ, Shi GX, Mao L, Cheng G, Jiang SJ, Ma XJ, An LZ, Du GZ, Collins Johnson N, Feng H. 2012. Direct and indirect influences of 8 yr of nitrogen and phosphorus fertilization on Glomeromycota in an alpine meadow ecosystem. New Phytologist 194: 523–535. McGonigle T, Miller M, Evans D, Fairchild G, Swan J. 1990. A new method which gives an objective measure of colonization of roots by vesicular– arbuscular mycorrhizal fungi. New Phytologist 115: 495–501. Oehl F, Laczko E, Bogenrieder A, Stahr K, B€osch R, van der Heijden M, Sieverding E. 2010. Soil type and land use intensity determine the composition of arbuscular mycorrhizal fungal communities. Soil Biology and Biochemistry 42: 724–738. Oksanen J, Blanchet FG, Kindt R, Legendre P, Minchin PR, O’Hara RB, Simpson GL, Solymo P, Stevens MHH, Wagner H. 2012. vegan: community ecology package. R package version 2.0-5. [WWW document] URL http://vegan. r-forge.r-project.org/ [accessed 11 July 2013]. Olsen SR, Cole CV, Watanabe FS, Dean LA. 1954. Estimation of available phosphorus in soils by extraction with sodium bicarbonate. USDA. Circular No. 939. € Opik M, Metsis M, Daniell T, Zobel M, Moora M. 2009. Large-scale parallel 454 sequencing reveals host ecological group specificity of arbuscular mycorrhizal fungi in a boreonemoral forest. New Phytologist 184: 424–437. Qian WH, He XL. 2009. Diversity of arbuscular mycorrhizal fungi associated with a desert plant Artemisia ordosica. Biodiverstiy Science 17: 506–511. R Development Core Team. 2012. Writing R extensions. Vienna, Austria: R Foundation for Statistical Computing [WWW document] URL http://cran. r-project.org/doc/manuals/R-exts.html/ [accessed 11 July 2013]. Reinhart KO, Wilson GWT, Rinella MJ. 2012. Predicting plant responses to mycorrhizae: integrating evolutionary history and plant traits. Ecology Letters 15: 689–695. Rillig MC, Mummey DL. 2006. Mycorrhizas and soil structure. New Phytologist 171: 41–53. Rosendahl S, McGee P, Morton JB. 2009. Lack of global population genetic differentiation in the arbuscular mycorrhizal fungus Glomus mosseae suggests a recent range expansion which may have coincided with the spread of agriculture. Molecular Ecology 18: 4316–4329. Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, Lesniewski RA, Oakley BB, Parks DH, Robinson CJ et al. 2009. Introducing mothur: open-source, platform independent, community-supported software for describing and comparing microbial communities. Applied and Environmental Microbiology 75: 7537–7541. Schnoor TK, Lekberg Y, Rosendahl S, Olsson PA. 2011. Mechanical soil disturbance as a determinant of arbuscular mycorrhizal fungal communities in semi-natural grassland. Mycorrhiza 21: 211–220. Sheng M, Lalande R, Hamel C, Ziadi N. 2013. Effect of long-term tillage and mineral phosphorus fertilization on arbuscular mycorrhizal fungi in a humid continental zone of Eastern Canada. Plant and Soil 369: 599–613. Ó 2014 The Authors New Phytologist Ó 2014 New Phytologist Trust

New Phytologist Simon L, Lalonde M, Bruns T. 1992. Specific amplification of 18S fungal ribosomal genes from vesicular–arbuscular endomycorrhizal fungi colonizing roots. Applied and Environmental Microbiology 58: 291–295. Smith SE, Read DJ. 2008. Mycorrhizal symbiosis. Cambridge, UK: Academic Press. St€ urmer SL, Siqueira JO. 2011. Species richness and spore abundance of arbuscular mycorrhizal fungi across distinct land uses in Western Brazilian Amazon. Mycorrhiza 21: 255–267. Tchabi A, Coyne D, Hountondji F, Lawouin L, Wiemken A, Oehl F. 2008. Arbuscular mycorrhizal fungal communities in sub-Saharan Savannas of Benin, West Africa, as affected by agricultural land use intensity and ecological zone. Mycorrhiza 18: 181–195. Tennant D. 1975. A test of a modified line intersect method of estimating root length. Journal of Ecology 63: 995–1001. Trouvelot A, Kough JL, Gianinazzi-Pearson V. 1986. Mesure du taux de mycorhization VA d’un syste‘me radiculaire. Recherche de me0 thodes d’estimation d’estimation ayant une signification fonctionnelle. In: Gianinazzi-Pearson V, Gianinazzi S, eds. Physiological and genetical aspects of mycorrhizae. Paris, France: INRA Press, 217–221. Verbruggen E, R€ oling WF, Gamper HA, Kowalchuk GA, Verhoef HA, van der Heijden MGA. 2010. Positive effects of organic farming on below-ground mutualists: large-scale comparison of mycorrhizal fungal communities in agricultural soils. New Phytologist 186: 968–979. Verbruggen E, Van Der Heijden MGA, Weedon JT, Kowalchuk GA, R€oling WF. 2012. Community assembly, species richness and nestedness of arbuscular mycorrhizal fungi in agricultural soils. Molecular Ecology 21: 2341–2353. Veresoglou SD, Caruso T, Rillig MC. 2012. Modelling the environmental and soil factors that shape the niches of two common arbuscular mycorrhizal fungal families. Plant and Soil 368: 507–518. Wagg C, Jansa J, Stadler M, Schmid B, Van Der Heijden MGA. 2011. Mycorrhizal fungal identity and diversity relaxes plant-plant competition. Ecology 92: 1303–1313. Walkley A. 1947. A critical examination of a rapid method for determining organic carbon in soils-effect of variations in digestion conditions and of inorganic soil constituents. Soil Science 63: 251–264. Wang JI, Xu X, Liu PF. 1999. Landuse and land carrying capacity in ecotone between africulture and animal husbandry in northern China. Resources science 21: 19–24 (in Chinese). Wardle DA, Bardgett RD, Klironomos JN, Setala H, van der Putten WH, Wall DH. 2004. Ecological linkages between aboveground and belowground biota. Science 304: 1629–1633. Warnes GR, Bolker B, Bonebakker L, Gentleman R, Huber W, Liaw A, Lumley T, Maechler M, Magnusson A, Moeller S et al. 2011. gplots: Various R programming tools for plotting data. [WWW document] URL http://cran. r-project.org/ [accessed 11 July 2013]. Wilson GW, Rice CW, Rillig MC, Springer A, Hartnett DC. 2009. Soil aggregation and carbon sequestration are tightly correlated with the abundance of arbuscular mycorrhizal fungi: results from long-term field experiments. Ecology Letters 12: 452–461. Yang HS, Yuan YG, Zhang Q, Tang JJ, Liu Y, Chen X. 2011. Changes in soil organic carbon, total nitrogen, and abundance of arbuscular mycorrhizal fungi along a large-scale aridity gradient. Catena 87: 70–77. Ye Y, Fang XQ. 2012. Expansion of cropland area and formation of the eastern farming-pastoral ecotone in northern China during the twentieth century. Regional Environmental Change 12: 923–934.

Ó 2014 The Authors New Phytologist Ó 2014 New Phytologist Trust

Research 11 Zhang L, Zhang J, Christie P, Li X. 2009. Effect of inoculation with the arbuscular mycorrhizal fungus Glomus intraradices on the root-knot nematode Meloidogyne incognita in cucumber. Journal of Plant Nutrition 32: 967–979.

Supporting Information Additional supporting information may be found in the online version of this article. Fig. S1 Precipitation map based on the 40 yr average annual records. Fig. S2 Temperature map based on the 40 yr average annual records. Fig. S3 Map where similar regions have been identified according to precipitation intervals. Fig. S4 Map where similar regions have been identified according to temperature intervals. Fig. S5 Neighbour-joining phylogenetic tree of representative sequences of each OTU detected in the study. Fig. S6 Rarefaction curves of Shannon–Weiner index for all samples. Table S1 Average annual precipitation and temperature and spatial coordinates of the sampling sites Table S2 Statistics of AMF sequences and OTUs Table S3 Relationships between AMF richness and land use type, soil properties and plant parameters Table S4 Relationships between AMF hyphal length density and land use type, soil properties and plant parameters Table S5 Relationships between AMF richness and hyphal length density with plant parameters Please note: Wiley Blackwell are not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing material) should be directed to the New Phytologist Central Office.

New Phytologist (2014) www.newphytologist.com