Linking the community structure of arbuscular mycorrhizal fungi and plants: a story of interdependence?

ABSTRACT Arbuscular mycorrhizal fungi (AMF) are crucial to plants and _vice versa_, but little is known about the factors linking the community structure of the two groups. We investigated

the association between AMF and the plant community structure in the nearest neighborhood of _Festuca brevipila_ in a semiarid grassland with steep environmental gradients, using

high-throughput sequencing of the Glomeromycotina (former Glomeromycota). We focused on the Passenger, Driver and Habitat hypotheses: (i) plant communities drive AMF (passenger); (ii) AMF

communities drive the plants (driver); (iii) the environment shapes both communities causing covariation. The null hypothesis is that the two assemblages are independent and this study

offers a spatially explicit novel test of it in the field at multiple, small scales. The AMF community consisted of 71 operational taxonomic units, the plant community of 47 species. Spatial

distance and spatial variation in the environment were the main determinants of the AMF community. The structure of the plant community around the focal plant was a poor predictor of AMF

communities, also in terms of phylogenetic community structure. Some evidence supports the passenger hypothesis, but the relative roles of the factors structuring the two groups clearly

differed, leading to an apparent decoupling of the two assemblages at the relatively small scale of this study. Community phylogenetic structure in AMF suggests an important role of

within-assemblage interactions. SIMILAR CONTENT BEING VIEWED BY OTHERS SPECIFICITY OF ASSEMBLAGE, NOT FUNGAL PARTNER SPECIES, EXPLAINS MYCORRHIZAL PARTNERSHIPS OF MYCOHETEROTROPHIC

_BURMANNIA_ PLANTS Article Open access 06 January 2021 THE IMPORTANCE OF THE PLANT MYCORRHIZAL COLLABORATION NICHE ACROSS SCALES Article 27 February 2025 PLANT PART AND A STEEP ENVIRONMENTAL

GRADIENT PREDICT PLANT MICROBIAL COMPOSITION IN A TROPICAL WATERSHED Article 13 November 2020 INTRODUCTION Arbuscular mycorrhizal fungi (AMF) are one of the most important symbiont groups

for plants, forming relationships with the majority of land plants and having a significant role in the acquisition of phosphorus (Smith and Read, 2008). Yet, despite some important progress

in recent years, especially in relation to interactions with other soil biota or how AMF respond to management (Alguacil et al., 2014; Caravaca and Ruess, 2014; Leifheit et al., 2015; Knegt

et al., 2016), there are many aspects of the assembly processes regulating the community ecology of these organisms that are poorly understood: a key challenge remains disentangling the

relative contribution of dispersal limitation, environmental filtering and biotic interaction on AMF community structure (Vályi et al., 2016). The cryptic nature of the group and the

complexity of the three-way interaction between plants, AMF and the environment complicate the study of the factors that regulate AMF community structure. Dispersal limitation remains one of

the most complex aspects of AMF ecology (Zobel and Öpik, 2014): as for example reviewed in Vályi et al. (2016), AMF can disperse via local mycelium spread but also spores, hyphal fragments

and colonized root fragments, and the importance of these mechanisms could be scale-dependent, although direct evidence is missing. Still, large AMF spores and hyphal fragments are mostly

spread via zoochory, which implies limited dispersal capability and seems reflected by small-scale patterns in community structure (Mummey and Rillig, 2008; Dumbrell et al., 2010a; Horn et

al., 2014). The effects of dispersal limitations are entangled with those of environmental gradients, biotic interactions within the AMF assemblage, and between AMF and plants (for example,

Mummey and Rillig, 2008; Dumbrell et al., 2010a; Horn et al., 2014; Martinez-Garcia et al., 2015; García de León et al., 2016a, 2016b). The study of AMF in grasslands is of particular

importance as grassland ecosystems cover a significant proportion of the earth’s surface, harbor the majority of herbaceous plant diversity (Shantz, 1954) and it is in grasslands that AMF

reach their highest abundance and diversity (Treseder and Cross, 2006; Kivlin et al., 2011). Studies on plant biodiversity in grassland ecosystems at small scales have revealed connections

between species richness of AMF and plants (Hiiesalu et al., 2014) and host plant effects on AMF community composition (Vályi et al., 2015). Still, effects can be very localized: AMF can

form extended hyphal networks but spatial autocorrelation in their distribution is typically found at submeter scales (Mummey and Rillig, 2008), with a potential role for biotic interactions

(Vályi et al., 2016). To date, only a few studies have taken this fact into account and applied a sufficiently fine-grained sampling design for a solid statistical analysis of the patterns

generated by local processes (Dumbrell et al., 2010b; Horn et al., 2014). AMF and plants form two sets of communities associated with each other but assembled through different processes

that take place at different spatial and temporal scales (Zobel and Öpik, 2014). The plant set can drive the fungal set or _vice versa_ (Figure 1), but which group is driving might depend on

successional stage, which is linked to differences in dispersal processes between plants and AMF. Zobel and Öpik (2014) have used the concept of difference in dispersal between AMF and

plants to revisit the Driver and Passenger hypotheses originally proposed by Hart et al. (2001). Zobel and Öpik (2014) also formulated the Habitat hypothesis to distinguish a situation where

AMF and plant communities covary but are not directly causally linked, as opposed to the null hypothesis of no covariation (‘independence’). For example, during primary succession, plants

typically arrive before AMF and then act as a potential filter to AMF: AMF are Passengers as they are following plants. However, dispersal limitation in an established AMF assemblage can

cause the AMF assemblage to more strongly determine which plants will establish during secondary succession: the AMF assemblage becomes the Driver (Zobel and Öpik, 2014). Zobel and Öpik

(2014) further predict that the Habitat hypothesis would be most common in regions with a stable community (for example, climax vegetation) where environmental variation within regions will

cause a non-mechanistic covariation between AMF and plant communities. The general null hypothesis is that plants and AMF may vary independently of each other, which could possibly happen at

very broad or global scales, where plants are more disperal limited than AMF seem to be (Kivlin et al., 2011; Öpik et al., 2013; Davison et al., 2015). Accordingly, Vályi et al. (2016) have

recently proposed that the host effect is minimal at regional and global scales. There are studies that have touched upon components of these hypotheses. For example, AMF taxa are generally

found to be able to colonize any AM (as opposed to non-AM) plant species (Klironomos, 2000), still there may be a bias towards easily cultivable species (Ohsowski et al., 2014) and

‘specificity’ might be quantitative rather than qualitative (Vályi et al., 2015). Therefore, AM fungal communities and plant communities may still be directly causally correlated despite the

perceived generalism of the AM symbiosis. A thorough account of the studies supporting the various hypotheses is given in Zobel and Öpik (2014) and we are aware of only two recent,

observational studies that have addressed the subject (Martinez-Garcia et al., 2015; García de León et al., 2016a). However, a problematic aspect of observational field studies remains to

tease apart cause and effect in the correlations between the two organism groups in the presence of spatial structure in the environment (Figure 1). To solve this problem, we applied a

spatially explicit design to sample AMF and plant communities along a replicated steep but short (≈15 m) soil environmental gradient (Horn et al., 2014). We could therefore control for

spatial patterns and environmental effects when testing for the effects of plants on AMF communities and _vice versa_. We used a standardized focal plant of high abundance to investigate

environmental, plant and AMF community variation at sufficiently small scales. We also took into account the phylogenetic community structure of both plant and AMF assemblages to allow

community relationships to occur at levels other than species/operational taxonomic unit (OTU) between and within the groups. Our main aim was to collect for the first time multiple scales

and high spatial resolution data to test the general null hypothesis that plant community structure, including phylogenetic structure, is independent of AMF community structure and _vice

versa_. If the hypothesis were rejected, given the scales included in the study, we aimed to collect support for one or more of the three alternative hypotheses (Figure 1), with the overall

goal of shedding light on the mutual relationships between plant and AMF communities. MATERIALS AND METHODS STUDY AREA AND SAMPLE COLLECTION Sampling was conducted in a nature protection

area located in north-eastern Germany (Brandenburg, 52°27.778'N, 14°29.349'E), a Natura 2000 biodiversity hotspot that contains over 200 different plant species and combines floral

elements of steppes and coastal habitats. Given the high diversity of plants (Ristow et al., 2011) and AMF (Horn et al., 2014), the area is very suitable for this study. We sampled by a

hierarchical nesting of plots in April 2011: twelve 3 × 3 m2 plots were sampled at the four corners of three 15 × 15 m2 larger plots (henceforth called ‘macroplots’) located on the slope of

a hillside (Supplementary Figure S1). The distances between the macroplots ranged from 20 to 500 m (Supplementary Figure S2), leading to overall intersample distances from a few cm to 3 m

(within a plot) and up to 500 m between macroplots. The uphill–downhill axes of the three macroplots were characterized by a steep textural gradient from sandy-loamy (uphill) to highly sandy

(downhill) soils (Supplementary Figure S3). Soil parameters varied significantly and to a large extent (for example, almost 3 units of pH) along the texture gradient (Horn et al., 2015). We

assessed the local AM fungal community in the roots and surrounding soil of _Festuca brevipila_ plants plus the neighboring plant species around these _Festuca_ plants. _F. brevipila_ is

one of the most abundant species in sampled plots (Ristow et al., 2011; Horn et al., 2015). Soil cores (5 cm radius, 15 cm deep) were taken from five _F. brevipila_ plants per plot,

resulting in 60 (5 plants x 12 plots) sampling locations. Each sample position was random within the plot (minimum distance of 30 cm between any two samples in the same plot; Supplementary

Figure S1). Plant presence/absence was assessed in the surrounding area in a radius of 15 cm around each soil core to target local interactions present in the rhizosphere of our focal plant

(neighborhood plant community structure). This scale is consistent with the minimal observed spatial autocorrelation of AM fungi (30–100 cm; Mummey and Rillig, 2008). Soil cores, including

roots and plant material, were stored at −20 °C before analysis. Each soil core was thoroughly homogenized and subsampled for soil chemical analyses (Supplementary Information, part a). We

measured water content, pH, carbon, nitrogen and phosphorus content of the soil, which are known to affect AMF community variation (Camenzind et al., 2014; Horn et al., 2014). Additionally,

dehydrogenase activity was assessed as a proxy for microbial activity. Roots were washed in Millipore water before analysis. DNA EXTRACTION, 454 PYROSEQUENCING AND OTU DELINEATION We

extracted genomic DNA twice from each core, once from 150 mg of washed, fine-ground _F. brevipila_ roots and once from 250 mg of soil material, which was sieved through a 2 mm mesh. We used

the PowerSoil DNA Isolation Kit (MoBio Laboratories Inc., Carlsbad, CA, USA) following the procedure in the manufacturer’s manual. We then created 454 pyrosequencing amplicon pools for the

AMF using a nested PCR design, using the AMF-specific primer set SSUmAf and LSUmAr for the first and SSUmCf and LSUmBr for the second, nested PCR (Krüger et al., 2009). The amplified region

spans genes for the SSU (small ribosomal subunit), the complete ITS (internal transcribed spacer) region and a part of the LSU (large ribosomal subunit). Subsequently, amplicons of ~600 bp

in length were created from the AMF-specific PCR fragments using general fungal primers located in the LSU gene modified with 454 adapters and sample-specific barcode sequences

(Supplementary Information, part b). The 454 sequencing was carried out on a Roche GS FLX+ system with titanium chemistry at the Göttingen Genomics Laboratory at the Georg-August University

of Göttingen (Göttingen, Germany). Sequences were denoized using the PyroNoise approach (Quince et al., 2009) implemented in Mothur (Schloss et al., 2009). The denoizing approach removes bad

quality sequences, creates sequence clusters and removes chimera sequences. After denoizing and preclustering, sequences from roots and soil were clustered into OTUs using CROP (Clustering

16S rRNA for OTU Prediction; Hao et al., 2011), which uses a Bayesian clustering algorithm. This approach addresses species delineation uncertainty better than hierarchical clustering

methods because of its flexible cutoff, thereby creating significantly less artifact OTUs than fixed cutoff clustering approaches (Hao et al., 2011). We checked the final OTU sequences

against chimeras using the Mothur implementation of the uchime algorithm and the Krüger et al. (2012) SSU-ITS-LSU alignment, as well as the slayer algorithm against the sequences themselves.

Default settings were used for both algorithms. Owing to the nature of pyrosequencing, we found differences in read numbers for every sampling location, so we resampled the read numbers to

equal amounts of 500 reads per sample using a bootstrap approach with 10 000 iterations per sample (Efron, 1979; Wehner et al., 2014). Samples with considerably lower read numbers than the

estimated resampling threshold (<350 reads, equal to 70% of the resampling threshold) were discarded before resampling. Additionally, singletons were removed. All subsequent statistical

analyses were carried out in R 3.1 (R Core Team, 2015). PHYLOGENETIC TREE CALCULATION OTUs were annotated according to the results of a BLAST search against the NCBI nucleotide database (nt)

before phylogenetic tree calculation. We calculated a phylogenetic tree for the AMF OTUs using RAxML (Stamatakis, 2006) to further refine the OTU definitions following our approach from a

previous study (Horn et al., 2014). About 110 representative sequences of an SSU-ITS-LSU AMF reference alignment (Krüger et al., 2012) plus an outgroup sequence from the Chytridiomycota were

added to our own sequences to determine the phylogenetic position of our OTUs. With the help of the phylogenetic tree, we removed sequences that clustered outside the Glomeromycotina and

are therefore likely to be erroneous or non-AMF sequences. NULL MODEL ANALYSIS AND PHYLOGENETIC COMMUNITY STRUCTURE To account for non-random species associations potentially linked to

biotic influences in AMF and plants, we performed null model analysis on plant and AMF species, respectively. Null models were created in EcoSim (Gotelli and Entsminger, 2012; for more

detail see Supplementary Information, part _c_) We included phylogenetic sorting of the respective communities as a potential driver of community structure (Horn et al., 2014). This approach

tests the hypothesis that the relationship between AMF and plant communities is reflected at a phylogenetic level including, but not restricted to species/OTUs. We analyzed phylogenetic

diversity within the AMF and plant communities separately. We chose the Daphne plant tree for our plant phylogenetic analysis (Durka and Michalski, 2012), which provides a complete set of

phylogenetic distances for our plant data set. Phylogenetic distances between AMF OTUs were calculated using the Needleman–Wunsch implementation of Esprit (Sun et al., 2009). The distances

between plant species were calculated as pairwise distances from the trimmed Daphne phylogenetic tree using the cophenetic.phylo function of the ape package (Paradis et al., 2004). Using the

picante package (Kembel et al., 2010), we obtained two estimates of phylogenetic diversity: the standardized effect size of mean pairwise distance (SES-MPD), which calculates the net

relatedness index from β-diversity with a null model; and intercommunity mean pairwise distance, that is phylogenetic distance between communities (Supplementary Information, part d). The

mean values of the net relatedness index of all samples of AMF were then used as the α-diversity measure to judge the clustering (positive) or segregation (negative) of the overall AMF or

plant community. Intercommunity mean pairwise distances were calculated as pairwise phylogenetic distances of the samples, based on pairwise genetic distances between OTUs and plant species.

To include the intercommunity mean pairwise distance information in a subsequent variance partitioning analysis (Legendre and Legendre, 1998; Caruso et al., 2012), the distance matrices of

plants and AMF were subjected to a principal coordinate analysis (PCoA), a generalization of ordinary principal component analysis (Legendre and Legendre, 1998) that is also the basis of

distance-based redundancy analysis. MODELS OF CORRELATIONS BETWEEN PLANTS AND AMF To test the null hypothesis of the study (that is, independence) robustly, we used three main multivariate

and multiple regression analysis based on redundancy analysis (Horn et al., 2015 and Supplementary Information, part e). Specifically, we quantified how plant community variation was

affected by variation in phylogenetic distance and community structure of AMF, and we also performed the _vice versa_ analysis using plant phylogenetic community structure and plant

community structure as a predictor of AM fungal community structure. To visualize patterns of community structure, we used PCoA. For AMF, PCoA was applied to Hellinger-transformed data to

prevent inflation in the weights of rare OTUs and work on an ecologically meaningful Euclidean space (Legendre and Legendre, 1998). For plants, PCoA was applied to the Jaccard distance

matrix of the presence/absence data. We also used the kriging estimator (Ribeiro and Diggle, 2001) to display spatial structures in environmental variables and the PCoA axes. PCoA axes of

the two assemblages were also plotted on a scatter plot to visualize correlation between the assemblages. We used Moran eigenvector mapping to account for spatial autocorrelation at multiple

scales (Dray et al., 2006; Legendre et al., 2009; Supplementary Information, part e): the analysis produces a number of vectors that describe spatial patterns in species distribution at all

the spatial scales resolvable by the sampling design. These vectors are sometimes referred to as ‘spatial factors’ or ‘spatial effects’, which implicitly describe spatial variation that may

originate from a multitude of factors such as spatially structured environmental variation but also spatial variation not related to environmental variation, and/or unmeasured but spatially

structured factors such as dispersal and biotic interactions. Spatial effects independent of environmental variables are often called ‘pure space’ (for example, Legendre and Legendre,

1998). We then used redundancy analysis and variance partitioning to test and quantify the effects of the community structure of one group on the other group by controlling for other

covarying effects (space, environment, phylogeny). Finally, to increase the statistical power of multivariate analysis (Warton et al., 2012) and so robustly test the null hypothesis, we also

tested the generalized linear response of the relative abundance of AM fungal taxa to the plant community and _vice versa_ using the manyglm function from the mvabund package (Wang et al.,

2012; Warton et al., 2012). The test was performed on residuals after removing the contributions of environmental and spatial covariates. All multivariate calculations were carried out in R,

using the vegan (Oksanen et al., 2012), the spacemakeR (Dray, 2011) and geoR (Ribeiro and Diggle, 2001) packages. RESULTS 454 PYROSEQUENCING AND OTU DELINEATION The clustered and denoized

data set consisted of 325 putative AM fungal OTUs. During the resampling, we removed seven root and one soil sample based on minimal read numbers of 500 reads. Species accumulation curves

showed a sufficient sampling depth (Supplementary Figures S4 and S5). After resampling and removal of singletons, 88 OTUs remained, of which 17 were removed since they clustered outside the

Glomeromycotina subphylum (former Glomeromycota, see Spatafora et al., 2016, after Schüßler et al., 2001) as it is currently described. This resulted in a total of 71 OTUs used in all

subsequent analyses. One representative sequence of each OTU is available from NCBI GenBank (https://www.ncbi.nlm.nih.gov/genbank/) under the accession numbers KX709382 to KX709452. The OTUs

found in our tree span all known AMF families, indicating a fairly exhaustive coverage of the Glomeromycotina subphylum (Supplementary Figure S5). The root data set eventually consisted of

68 OTUs and the soil data set of 62 OTUs. Overall OTU richness per macroplot was comparable between these data sets, ranging from 30 to 43 in roots and from 28 to 43 in soil (Table 1). The

dominant fungal groups in our soils and roots were _Glomus_ spp. and _Rhizophagus_ spp. COMMUNITY STRUCTURE OF AMF EXCLUDING PLANTS The AMF community was significantly segregated at the

level of the entire data set. However, for the AMF communities in root samples, the effect was significant only for one of the macroplots and the whole data set (Table 1). For the soil

community two out of three macroplots had significantly segregated assemblages and effect sizes were considerably higher in soil than in root data sets (Table 1). There were no significant

net relatedness index differences overall. Neither the root nor the soil sets of the phylogenetic data showed significantly segregated or aggregated communities on a per-macroplot or

per-data-set basis. All measured environmental variables display a clear spatial gradient along the uphill direction (see four examples in Figure 2), although sometimes with an additional

component of variation along the direction orthogonal to the uphill direction. At the macroplot scale, the spatial gradient in the first two axes of the PCoA of AMF (accounting for almost

2/3 of total variance) follow the environmental gradient more than the equivalent PCoA axis of plants do (Figure 3). When we excluded plants from the analysis and removed spatial effects,

the effect of the measured environmental variables (pH, water content, C, N, C/N ratio, phosphorus, dehydrogenase activity) on AMF community structure was overall low. With an exception of

the root data set from one macroplot, environmental data explained <10%. Pure space was a major predictor of the overall data set and within each macroplot, showing significant and large

proportions (up to 31%) of explained variation (Supplementary Table S2). Phylogeny was the second largest explanatory component in the variance partitioning of the AMF without plants and up

to 30% of variation could be explained by the phylogenetic distance of the AMF in our data set (Supplementary Table S2). Additionally, we found the spatial-phylogenetic effects accounted for

a large fraction of the AMF variance. AMF–PLANT CORRELATIONS A PCoA ordination of all samples from all plots show that the plant assemblage seemed the most structured spatially: macroplot 3

clustered separately from macroplot 1 and 2 (see also Figure 4). The same clustering was not observed in AMF as clearly as in plants, neither in roots nor in soil. Scatter plots (Figure 5)

of the first two PCoAs of AMF and plants revealed that gradients in the community structure of the two assemblages are correlated but with a confounding effect of spatial patterns at the

broad scale separating the three macroplots (see for example Figures 5a and c). Still, after filtering out spatial autocorrelation, plant community structure accounted for a statistically

significant amount of variation in the root AMF community, while plant phylogeny was not a significant predictor (Table 2). Instead, when we used the AMF community as a predictor of the

plant community, the variation explained by the fungi was very low and not significant (Supplementary Table S3). Overall, these results reject the null hypothesis of the study, although the

amount of variation uniquely attributable to the effect of plants on AMF is small (Table 2). GLM results were consistent with these results: plant community structure had significant effects

on the AMF community in roots (_P_<0.001) and soil (_P_<0.001), but AMF communities did not show any significant effects when used as a predictor of plant community structure.

DISCUSSION IS THE COMMUNITY STRUCTURE OF AMF INDEPENDENT OF THAT OF PLANTS? AMF and plants may affect each other's community dynamics depending on spatial and temporal scale, the latter

especially in relation to succession (Zobel and Öpik, 2014). Evaluating which group is driving which other group is challenging because both groups may influence each other to some extent

and possibly at different spatial and temporal scales (Martinez-Garcia et al., 2015; García de León et al., 2016a). Also, in a stable ecosystem (for example, climax) regional covariation

between AMF and plants could arise as the effect of environmental gradients (Habitat hypothesis). Our results reflect this complexity of plant–AMF interactions in a species-rich grassland

area at a range of small spatial scales but made clear some important points. First, AMF community variance is mostly accounted for by spatial factors and phylogenetic distance patterns in

OTU composition. Second, plant communities were also strongly influenced by the soil environment, but AMF communities were not. Overall, AMF and plants showed different spatial structures

and the relative roles of the tested factors clearly change between plant and AMF, which rules out the Habitat hypothesis. The strong influence of spatial factors on AMF communities aligns

with the Driver hypothesis, but we did not find an effect of AMF on plants, thus refuting this hypothesis (Zobel and Öpik, 2014). Instead, when plant communities were used as a predictor of

AMF, after taking into account all other effects (that is, environment, space), we found a significant effect of plants on AMF communities. We can thus reject the statistical null hypothesis

that the groups are independent. Specifically, there is some support for AMF acting as Passengers. We have to note that reversing response and predictors (that is, AMF passenger or driver)

in these multivariate statistical models is not trivial. For example, there is additional and not invertible information in the phylogenetic trees of each set of species. Notwithstanding the

aforementioned technicality and the statistical rejection of the null hypothesis, the complex set of correlations linking plants and AMF are relatively weak (whatever group plays the role

of predictor or response), which implies that the interaction between plants and AMF are weak at the community level: plant community structure remains a modest predictor of AMF community

structure compared with the other predictors employed in the analysis. All these results are overall consistent with theoretical predictions put forward by Zobel and Öpik (2014): the scale

of the study is relatively small, with a steep but short soil environmental gradient replicated a number of times at various distances (within plots and between plots), from tens of meters

to a few hundred meters. At these scales, we can expect the absence of or weak dispersal limitation for plants but some dispersal limitation in AMF, and the texture gradient sampled along

the hills may mimic a primary succession gradient in the plant assemblage (Horn et al., 2015). Under these conditions, the passenger ‘effect’ should be at its strongest. Which further

mechanisms could underlie the observed patterns? More specifically, if AMF are passengers, why is the effect of plants apparently weak? It has been shown that plants may reward the best

fungal partners with more carbohydrates (Bever et al., 2009; Kiers et al., 2011; Verbruggen et al., 2012) and that particular plant communities may cause the development of specific AMF

communities (Hausmann and Hawkes, 2009). This is consistent with our observation that the neighborhood plant community of a dominant focal plant is a significant but not very strong

predictor of the AMF community in its roots. Interestingly, we observed this effect only for the root assemblage and not for the soil assemblage and plant community phylogenetic structure

seems to have no role in these effects. The weakness of the observed effects of plant communities on AMF communities may be particular to the study system. For instance, the dominance of

_Glomus_ spp., _Rhizophagus irregularis_ and other generalist taxa may cause effects to be less strong than in systems with higher evenness and/or specialist taxa. Another potential

explanation is that other ecological interactions overwhelm the effect, as evidenced from the non-random phylogenetic community pattern of the AMF assemblage. Also, the grassland is

dominated by several C3 grasses, which are not very dependent on mycorrhiza (Reinhart et al., 2012), and there is increasing evidence that these plants associate with generalist AMF taxa

(Helgason et al., 2007; Öpik et al., 2009; Vályi et al., 2015). ARE AMF COMMUNITIES ASSEMBLED THROUGH INTERSPECIFIC INTERACTIONS? As recently reviewed by Vályi et al. (2016), AMF communities

are structured by a range of different processes, including environmental filtering, dispersal and biotic interactions (Lekberg et al., 2007; Peng et al., 2009; Dumbrell et al., 2010a,

2010b; Silva and Batalha, 2011). Biotic interaction at the interspecific level could have a major role in some cases. For example, negative interactions between AMF species competing for the

same root space may result in the superior competitor persisting in the root (Hart et al., 2001; Thonar et al., 2014). In addition, greenhouse studies as well as field observational work

have shown that net phylogenetic distance patterns can predict co-occurrence (Maherali and Klironomos, 2007; Horn et al., 2014) and AMF traits are phylogenetically conserved (Powell et al.,

2009). For example, mechanisms such as facilitation or feedbacks between plants and AMF could be signaled by net phylogenetic distance patterns in community structure if closely related

species received similar facilitation (Anacker et al., 2014). Here, the AMF assemblage was strongly segregated, while phylogenetic aggregation or segregation patterns were not significant,

but with overall quite low mean pairwise distances between communities. This slightly contrasts with a previous analysis of AMF communities in the same sampling area as well as findings from

other authors, which show local species pools to be phylogenetically clustered (Kivlin et al., 2011; Saks et al., 2014; Horn et al., 2014; Grilli et al., 2015). At the same time, when we

excluded plants from the variance partitioning of AMF community matrix, up to 30% of AMF community variation could be explained by phylogenetic distance (Supplementary Table S2). Integrating

all the available evidence (Kivlin et al., 2011; Saks et al., 2014; Horn et al., 2014; Grilli et al., 2015), including previous work from this site (Horn et al., 2014), AMF communities seem

phylogenetically structured and very much spatially structured. Given the amount of variation accounted for by these effects and the fact that for plants environmental variation was the

main structuring factor, we conclude that AMF communities in our sampling area assembled mostly independently of the plant community with a possibly important role of interactions within the

AMF community. However, there is shared variation between environment, space and phylogenetically structured variation in AM fungal communities. The processes behind shared variation (for

example, spatially structured covariation between environmental and phylogenetic variation) cannot be explained solely on the basis of observational evidence. Experimental work will in the

future be necessary to understand how this shared variation is generated. As already suggested by Zobel and Öpik (2014), in an ideal experiment either the plant or AMF community should be

kept constant while varying the other community, and also in relation to changing environmental conditions (for example, soil properties such as pH) and different degrees of dispersal

limitation. These experiments are challenging under field conditions, but we suggest that surveying AMF communities in plant assemblages under a range of primary and secondary succession

stages (for example, García de León et al., 2016a) and manipulating vegetation to control the succession process will offer a valid starting point to move from patterns to the mechanisms. In

that perspective, our study suggests to test for a potentially important role of biotic interactions within the AMF assemblage. REFERENCES * Alguacil MM, Torrecillas E, Garcia-Orenes F,

Roldan A . (2014). Changes in the composition and diversity of AMF communities mediated by management practices in a Mediterranean soil are related with increases in soil biological

activity. _Soil Biol Biochem_ 76: 34–44. Article  CAS  Google Scholar  * Anacker BL, Klironomos JN, Maherali H, Reinhart KO, Strauss SY . (2014). Phylogenetic conservatism in plant-soil

feedback and its implications for plant abundance. _Ecol Lett_ 17: 1613–1621. Article  Google Scholar  * Bever JD, Richardson SC, Lawrence BM, Holmes J, Watson M . (2009). Preferential

allocation to beneficial symbiont with spatial structure maintains mycorrhizal mutualism. _Ecol Lett_ 12: 13–21. Article  Google Scholar  * Camenzind T, Hempel S, Homeier J, Horn S, Velescu

A, Wilcke W _et al_. (2014). Nitrogen and phosphorus additions impact arbuscular mycorrhizal abundance and molecular diversity in a tropical montane forest. _Glob Chang Biol_ 20: 3646–3659.

Article  Google Scholar  * Caravaca F, Ruess L . (2014). Arbuscular mycorrhizal fungi and their associated microbial community modulated by Collembola grazers in host plant free substrate.

_Soil Biol Biochem_ 69: 25–33. Article  CAS  Google Scholar  * Caruso T, Hempel S, Powell JR, Barto EK, Rillig MC . (2012). Compositional divergence and convergence in arbuscular mycorrhizal

fungal communities. _Ecology_ 93: 1115–1124. Article  CAS  Google Scholar  * Davison J, Moora M, Öpik M, Adholeya A, Ainsaar L, Bâ A _et al_. (2015). Global assessment of arbuscular

mycorrhizal fungus diversity reveals very low endemism. _Science_ 349: 970–973. Article  CAS  Google Scholar  * García de León D, Moora M, Öpik M, Neuenkamp L, Gerz M, Jairus T _et al_.

(2016a). Symbiont dynamics during ecosystem succession: co-occurring plant and arbuscular mycorrhizal fungal communities. _FEMS Microbiol Ecol_ 92: fiw097. * García de León D, Moora M, Öpik

M, Jairus T, Neuenkamp L, Vasar M _et al_. (2016b). Dispersal of arbuscular mycorrhizal fungi and plants during succession. _Acta Oecol_ 77: 128–135. Article  Google Scholar  * Dray S .

(2011). SpacemakeR: spatial modelling. R package version 0.0-5/r10. Available at: http://R-Forge.R-project.org/projects/sedar/ (accessed on 1 May 2015). * Dray S, Legendre P, Peres-Neto PR .

(2006). Spatial modelling: a comprehensive framework for principal coordinate analysis of neighbour matrices (PCNM). _Ecol Model_ 196: 483–493. Article  Google Scholar  * Dumbrell AJ,

Nelson M, Helgason T, Dytham C, Fitter AH . (2010a). Idiosyncrasy and overdominance in the structure of natural communities of arbuscular mycorrhizal fungi: is there a role for stochastic

processes? _J Ecol_ 98: 419–428. Article  Google Scholar  * Dumbrell AJ, Nelson M, Helgason T, Dytham C, Fitter AH . (2010b). Relative roles of niche and neutral processes in structuring a

soil microbial community. _ISME J_ 4: 337–345. Article  Google Scholar  * Durka W, Michalski SG . (2012). Daphne: a dated phylogeny of a large European flora for phylogenetically informed

ecological analyses. _Ecology_ 93: 2297–2297. Article  Google Scholar  * Efron B . (1979). Bootstrap methods: another look at the jackknife. _Ann Statist_ 7: 1–26. Article  Google Scholar  *

Gotelli NJ, Entsminger GL . (2012) _EcoSim 7.72_. Acquired Intelligence Inc. & Kesey-Bear: Jericho, VT, USA. * Grilli G, Urcelay C, Galetto L, Davison J, Vasar M, Saks Ü _et al_.

(2015). The composition of arbuscular mycorrhizal fungal communities in the roots of a ruderal forb is not related to the forest fragmentation process. _Environ Microbiol_ 17: 2709–2720.

Article  Google Scholar  * Hao X, Jiang R, Chen T . (2011). Clustering 16S rRNA for OTU prediction: a method of unsupervised Bayesian clustering. _Bioinformatics_ 27: 611–618. Article  CAS 

Google Scholar  * Hart MM, Reader RJ, Klironomos JN . (2001). Life-history strategies of arbuscular mycorrhizal fungi in relation to their successional dynamics. _Mycologia_ 93: 1186–1194.

Article  Google Scholar  * Hausmann NT, Hawkes CV . (2009). Plant neighborhood control of arbuscular mycorrhizal community composition. _New Phytol_ 183: 1188–1200. Article  Google Scholar 

* Helgason T, Merryweather JW, Young JPW, Fitter AH . (2007). Specificity and resilience in the arbuscular mycorrhizal fungi of a natural woodland community. _J Ecol_ 95: 623–630. Article 

CAS  Google Scholar  * Hiiesalu I, Pärtel M, Davison J, Gerhold P, Metsis M, Moora M _et al_. (2014). Species richness of arbuscular mycorrhizal fungi: associations with grassland plant

richness and biomass. _New Phytol_ 203: 233–244. Article  CAS  Google Scholar  * Horn S, Caruso T, Verbruggen E, Rillig MC, Hempel S . (2014). Arbuscular mycorrhizal fungal communities are

phylogenetically clustered at small scales. _ISME J_ 8: 2231–2242. Article  CAS  Google Scholar  * Horn S, Hempel S, Ristow M, Rillig MC, Kowarik I, Caruso T . (2015). Plant community

assembly at small scales: Spatial vs. environmental factors in a European grassland. _Acta Oecol_ 63: 56–62. Article  Google Scholar  * Kembel SW, Cowan PD, Helmus MR, Cornwell WK, Morlon H,

Ackerly DD _et al_. (2010). Picante: R tools for integrating phylogenies and ecology. _Bioinformatics_ 26: 1463–1464. Article  CAS  Google Scholar  * Kiers ET, Duhamel M, Beesetty Y, Mensah

JA, Franken O, Verbruggen E _et al_. (2011). Reciprocal rewards stabilize cooperation in the mycorrhizal symbiosis. _Science_ 333: 880–882. Article  CAS  Google Scholar  * Kivlin SN, Hawkes

CV, Treseder KK . (2011). Global diversity and distribution of arbuscular mycorrhizal fungi. _Soil Biol Biochem_ 43: 2294–2303. Article  CAS  Google Scholar  * Klironomos J . (2000).

Host-specificity and functional diversity among arbuscular mycorrhizal fungi. Microbial Biosystems: New Frontiers. _Proceedings of the 8th International Symposium on Microbial Ecology_;

Halifax, Nova Scotia, Canada. Atlantic Canada Society for Microbial Ecology. * Knegt B, Jansa J, Franken O, Engelmoer DJP, Werner GDA, Bücking H _et al_. (2016). Host plant quality mediates

competition between arbuscular mycorrhizal fungi. _Fungal Ecol_ 20: 233–240. Article  Google Scholar  * Krüger M, Krüger C, Walker C, Stockinger H, Schüßler A . (2012). Phylogenetic

reference data for systematics and phylotaxonomy of arbuscular mycorrhizal fungi from phylum to species level. _New Phytol_ 193: 970–984. Article  Google Scholar  * Krüger M, Stockinger H,

Krüger C, Schüßler A . (2009). DNA-based species level detection of Glomeromycota: one PCR primer set for all arbuscular mycorrhizal fungi. _New Phytol_ 183: 212–223. Article  Google Scholar

  * Legendre P, Legendre L . (1998) _Numerical Ecology_. Elsevier Science: Amsterdam. Google Scholar  * Legendre P, Mi XC, Ren HB, Ma KP, Yu MJ, Sun IF _et al_. (2009). Partitioning beta

diversity in a subtropical broad-leaved forest of China. _Ecology_ 90: 663–674. Article  Google Scholar  * Leifheit EF, Verbruggen E, Rillig MC . (2015). Arbuscular mycorrhizal fungi reduce

decomposition of woody plant litter while increasing soil aggregation. _Soil Biol Biochem_ 81: 323–328. Article  CAS  Google Scholar  * 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. _J Ecol_ 95: 95–105. Article  Google Scholar  * Maherali H, Klironomos

JN . (2007). Influence of phylogeny on fungal community assembly and ecosystem functioning. _Science_ 316: 1746–1748. Article  CAS  Google Scholar  * Martinez-Garcia LB, Richardson SJ,

Tylianakis JM, Peltzer DA, Dickie IA . (2015). Host identity is a dominant driver of mycorrhizal fungal community composition during ecosystem development. _New Phytol_ 205: 1565–1576.

Article  CAS  Google Scholar  * Mummey DL, Rillig MC . (2008). Spatial characterization of arbuscular mycorrhizal fungal molecular diversity at the submetre scale in a temperate grassland.

_FEMS Microbiol Ecol_ 64: 260–270. Article  CAS  Google Scholar  * Ohsowski BM, Zaitsoff PD, Opik M, Hart MM . (2014). Where the wild things are: looking for uncultured Glomeromycota. _New

Phytol_ 204: 171–179. Article  Google Scholar  * Oksanen J, Guillaume Blanchet F, Kindt R, Legendre P, Minchin PR, O'Hara RB _et al_. (2012). vegan: Community ecology package. R package

version 2.0-10. Available at: https://cran.r-project.org/web/packages/vegan/index.html (accessed on 20 January 2015). * Öpik M, Metsis M, Daniell TJ, Zobel M, Moora M . (2009). Large-scale

parallel 454 sequencing reveals host ecological group specificity of arbuscular mycorrhizal fungi in a boreonemoral forest. _New Phytol_ 184: 424–437. Article  Google Scholar  * Öpik M,

Zobel M, Cantero JJ, Davison J, Facelli JM, Hiiesalu I _et al_. (2013). Global sampling of plant roots expands the described molecular diversity of arbuscular mycorrhizal fungi. _Mycorrhiza_

23: 411–430. Article  Google Scholar  * Paradis E, Claude J, Strimmer K . (2004). APE: analyses of phylogenetics and evolution in R language. _Bioinformatics_ 20: 289–290. Article  CAS 

Google Scholar  * Peng Y, Chen G, Tian G, Yang X . (2009). Niches of plant populations in mangrove reserve of Qi’ao Island, Pearl River Estuary. _Acta Ecol Sin_ 29: 357–361. Article  Google

Scholar  * Powell JR, Parrent JL, Hart MM, Klironomos JN, Rillig MC, Maherali H . (2009). Phylogenetic trait conservatism and the evolution of functional trade-offs in arbuscular mycorrhizal

fungi. _Proc R Soc Lond Ser B_ 276: 4237–4245. Article  Google Scholar  * Quince C, Lanzen A, Curtis TP, Davenport RJ, Hall N, Head IM _et al_. (2009). Accurate determination of microbial

diversity from 454 pyrosequencing data. _Nat Meth_ 6: 639–U627. Article  CAS  Google Scholar  * R Core Team. (2015) _R: A Language and Environment for Statistical Computing_. R Foundation

for Statistical Computing: Vienna, Austria. * Reinhart KO, Wilson GWT, Rinella MJ . (2012). Predicting plant responses to mycorrhizae: integrating evolutionary history and plant traits.

_Ecol Lett_ 15: 689–695. Article  Google Scholar  * Ribeiro Jr PJ, Diggle PJ . (2001). geoR: a package for geostatistical analysis. _R-NEWS_ 1: 15–18. Google Scholar  * Ristow M, Rohner M-S,

Heinken T . (2011). Die Oderhänge bei Mallnow und Lebus. _Tuexenia Beih (Flora und Vegetation in Brandenburg)_ 4: 127–144. Google Scholar  * Saks Ü, Davison J, Öpik M, Vasar M, Moora M,

Zobel M . (2014). Root-colonizing and soil-borne communities of arbuscular mycorrhizal fungi in a temperate forest understorey. _Botany_ 92: 277–285. Article  Google Scholar  * Schloss PD,

Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB _et al_. (2009). Introducing Mothur: Open-source, platform-independent, community-supported software for describing and comparing

microbial communities. _Appl Environ Microbiol_ 75: 7537–7541. Article  CAS  Google Scholar  * Schüßler A, Schwarzott D, Walker C . (2001). A new fungal phylum, the Glomeromycota: phylogeny

and evolution. _Mycol Res_ 105: 1413–1421. Article  Google Scholar  * Shantz HL . (1954). The place of grasslands in the Earth's cover. _Ecology_ 35: 3. Article  Google Scholar  * Silva

IA, Batalha MA . (2011). Plant functional types in Brazilian savannas: the niche partitioning between herbaceous and woody species. _Perspect Plant Ecol Evol Syst_ 13: 201–206. Article 

Google Scholar  * Smith SE, Read DJ . (2008) _Mycorrhizal symbiosis_, 3rd edn. Academic Press: Burlington, MA, USA. Google Scholar  * Spatafora JW, Chang Y, Benny GL, Lazarus K, Smith ME,

Berbee ML _et al_. (2016). A phylum-level phylogenetic classification of zygomycete fungi based on genome-scale data. _Mycologia_ 108: 1028–1046. Article  CAS  Google Scholar  * Stamatakis A

. (2006). RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models. _Bioinformatics_ 22: 2688–2690. Article  CAS  Google Scholar  * Sun YJ, Cai

YP, Liu L, Yu FH, Farrell ML, McKendree W _et al_. (2009). ESPRIT: estimating species richness using large collections of 16S rRNA pyrosequences. _Nucleic Acids Res_ 37: e76. * Thonar C,

Frossard E, Smilauer P, Jansa J . (2014). Competition and facilitation in synthetic communities of arbuscular mycorrhizal fungi. _Mol Ecol_ 23: 733–746. Article  Google Scholar  * Treseder

KK, Cross A . (2006). Global distributions of arbuscular mycorrhizal fungi. _Ecosystems_ 9: 305–316. Article  Google Scholar  * Verbruggen E, El Mouden C, Jansa J, Akkermans G, Bucking H,

West SA _et al_. (2012). Spatial structure and interspecific cooperation: theory and an empirical test using the mycorrhizal mutualism. _Am Nat_ 179: E133–E146. Article  Google Scholar  *

Vályi K, Mardhiah U, Rillig MC, Hempel S . (2016). Community assembly and coexistence in communities of arbuscular mycorrhizal fungi. _ISME J_ 10: 2341–2351. Article  Google Scholar  * Vályi

K, Rillig MC, Hempel S . (2015). Land-use intensity and host plant identity interactively shape communities of arbuscular mycorrhizal fungi in roots of grassland plants. _New Phytol_ 205:

1577–1586. Article  Google Scholar  * Wang Y, Naumann U, Wright ST, Warton DI . (2012). mvabund—an R package for model-based analysis of multivariate abundance data. _Methods Ecol Evol_ 3:

471–474. Article  Google Scholar  * Warton DI, Wright ST, Wang Y . (2012). Distance-based multivariate analyses confound location and dispersion effects. _Methods Ecol Evol_ 3: 89–101.

Article  Google Scholar  * Wehner J, Powell JR, Muller LAH, Caruso T, Veresoglou SD, Hempel S _et al_. (2014). Determinants of root-associated fungal communities within Asteraceae in a

semi-arid grassland. _J Ecol_ 102: 425–436. Article  Google Scholar  * Zobel M, Öpik M . (2014). Plant and arbuscular mycorrhizal fungal (AMF) communities—which drives which? _J Veg Sci_ 25:

1133–1140. Article  Google Scholar  Download references ACKNOWLEDGEMENTS SH and TC acknowledge funding by the German science foundation (DFG Grant No. CA 987/1-1). TC was also supported by

the project SENSE (Structure and Ecological Niche in the Soil Environment; EC FP7—631399—SENSE). We are grateful to four anonymous reviewers for their invaluable comments and suggestions,

which have improved the quality of this work. Support during the 454 sequencing by the Göttingen Genomics Laboratory is gratefully acknowledged. AUTHOR INFORMATION AUTHORS AND AFFILIATIONS *

Hawkesbury Institute for the Environment, Western Sydney University, Penrith, NSW, Australia Sebastian Horn * Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin,

Germany Sebastian Horn, Stefan Hempel & Matthias C Rillig * Institut für Biologie—Ökologie der Pflanzen, Freie Universität Berlin, Berlin, Germany Stefan Hempel & Matthias C Rillig *

Department of Biology, Research group of Plant and Vegetation Ecology (PLECO), University of Antwerp, Wilrijk, Belgium Erik Verbruggen * School of Biological Sciences and Institute for

Global Food Security, Queen’s University Belfast, Medical Biology Centre, Belfast, Northern Ireland, UK Tancredi Caruso Authors * Sebastian Horn View author publications You can also search

for this author inPubMed Google Scholar * Stefan Hempel View author publications You can also search for this author inPubMed Google Scholar * Erik Verbruggen View author publications You

can also search for this author inPubMed Google Scholar * Matthias C Rillig View author publications You can also search for this author inPubMed Google Scholar * Tancredi Caruso View author

publications You can also search for this author inPubMed Google Scholar CORRESPONDING AUTHOR Correspondence to Tancredi Caruso. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare

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KB) RIGHTS AND PERMISSIONS Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Horn, S., Hempel, S., Verbruggen, E. _et al._ Linking the community structure of arbuscular

mycorrhizal fungi and plants: a story of interdependence?. _ISME J_ 11, 1400–1411 (2017). https://doi.org/10.1038/ismej.2017.5 Download citation * Received: 25 November 2015 * Revised: 14

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