It is common for 454 sequencing studies to report considerably higher diversity than cloning and sequencing studies (Öpik et al. 2009; Becklin, Hertweck & Jumpponen 2012). Our 156 816 sequences resulted in a large number of OTUs, considering that we focused on root-associated fungi of plants in a single plant family (the Asteraceae) in a small area. We attribute this to the high plant diversity in the investigated area, which is a biodiversity hotspot in Germany with about 200 plant species. The grassland is quite heterogeneous consisting of small hills with steep slopes and interspersed woodland stands, which may provide a range of different microhabitats for fungal colonization.
The number of sequence reads varied strongly between the individuals. This variability could be caused by errors during the amplification or during sequencing. For example, a fungal community on a certain plant individual, which has an unequal distribution of short and long ITS fragments could introduce amplification errors during PCRs, since shorter DNA fragments are preferentially amplified (Bellemain et al. 2010). Another potential factor causing this variation could be the barcodes used (Hamady et al. 2008). However, we used a resampling procedure to correct for these differences in our analysis.
The plant species with the highest number of OTUs was H. pilosella, while the species with the lowest number was H. tuberosus. The low number of OTUs in Helianthus might be due to its root system, a tuber-forming root with a relatively coarse architecture, compared to the highly branched root system of Hieracium, which might therefore be more susceptible to fungal infection (Newsham, Fitter & Watkinson 1995).
Taxonomic composition of the fungal communities
Most of the OTUs (35.8%) belonged to the Ascomycota, which are commonly found to be the dominant root-colonizing fungal group in semi-arid grasslands (Porras-Alfaro et al. 2011). The most abundant OTU in our sampling area was identified as closely related to P. mustea, the function of which is not well understood. Phialophora species are known to form a complex group of fungi with endophytes, saprobes and plant pathogens (Yan, Rogers & Wang 1995; Ko et al. 2011). The second most abundant fungus was identified as related to Paraphoma chrysanthemicola, section Phoma (de Gruyter et al. 2010), a common dark septate endophyte in grasslands (Porras-Alfaro et al. 2011). The roles of these endophytes in ecosystems still remain unclear but under controlled condition, they can enhance plant performance (Newsham 2011).
Surprisingly, we also found a relatively large number of Basidiomycota (18.3%), which are typically more frequent in forest soils (Buée et al. 2009). The relative abundance we report is almost double the relative abundance that Porras-Alfaro et al. (2011) retrieved in their study, conducted in a semi-arid grassland. The most abundant genus of Basidiomycota and the third most abundant overall was Sebacina (Order Sebacinales). Sebacinalean fungi are common endophytes in many plant roots (Selosse, Dubois & Alvarez 2009; Weiss et al. 2011) and may enhance plant growth and pathogen protection (Schäfer, Khatabi & Kogel 2007). The AM fungus R. irregularis (Phylum Glomeromycota) was relatively abundant, and among the 50 most abundant sequences. In total, Glomeromycota accounted for 7.2% of all OTUs. Glomeromycota are widespread and dominant in grasslands, and Asteraceae generally form symbioses with them (Hempel et al. 2013).
We also found that 22.6% of OTUs were from fungi of uncertain identity, which likely reflects the fact that only a low percentage of fungi have been formally described (Mueller & Schmit 2007).
Predictors explaining community composition
The multivariate distribution of fungal species, which is an aspect of beta-diversity (Legendre, Borcard & Peres-Neto 2005), could be explained to a certain extent by space, host phylogeny, soil type and sampling time; all four predictors had a significant influence on the fungal communities.
However, the most important factor explaining total fungal community composition was ‘host phylogeny’, accounting for 20% of the variance and suggesting that closely related species shared more similar fungal communities than expected by chance (i.e. root-associated fungal communities exhibited conservatism across the host phylogeny). The cluster analysis showed that fungal community composition tended to be more similar among individuals of the same plant species. Such a relationship between host identity/phylogeny and associated fungi has been observed for plant pathogenic fungi, like the anther smut fungi and their caryophyllaceous hosts (Refrégier et al. 2008) and necrotrophic leaf fungi (Gilbert & Webb 2007). Similar relationships have also been observed for mycorrhizal fungal associations in Orchis (Jacquemyn et al. 2011) and ECM fungi (Tedersoo et al. 2013).
Fungal colonization of host plants is likely to be limited or facilitated by certain plant traits, which may be shared among closely related plant species. For example, root traits linked to the architecture of the root system (e.g. root length, root diameter, ratio of fine and coarse roots) and foraging behaviour, which exhibit phylogenetic constraints (Kembell & Cahill 2005), are likely to influence fungal colonization due to variation in surface area-to-volume ratios and the frequency of infection points (Newsham, Fitter & Watkinson 1995). For example, very dense root systems with many fine roots might be more susceptible to fungal infections compared to less branched, thick roots (Newsham, Fitter & Watkinson 1995).
On the other hand, this phylogenetic signal in fungal community composition could be simply due to patterns of co-occurrence, with fungi and their hosts limited by the same or covarying environmental conditions. However, our results suggest this is not the case since 20% of the variation accounted for phylogeny was independent of the other predictors. Furthermore, in a recent study, Tedersoo et al. (2013) found similar effects of host plant phylogeny on fungal community composition within the Salicaceae, which indicates that our pattern might be independent of the investigated system.
The second most important factor for structuring the fungal communities was space, which could be due to a strong fragmentation of the landscape, dispersal dynamics or an interaction between these dynamics and environmental drivers not accounted for by our soil factor. The alternation of hills and valleys might limit dispersal and gene flow between the communities on the roots of the different host plants. For example, for AM fungi, habitat filtering and dispersal limitation are both drivers of assembly processes (Kivlin, Hawkes & Treseder 2011; Caruso et al. 2012), but the total amount of variation in species turnover explained by environmental parameters (edaphic variables, host species, etc.) may be small due to broad tolerances in some taxa (Powell et al. 2011).
Despite the statistical significance of differences in soil type, we only detected comparably small effects of soil type. This may imply generally weak niche differentiation with respect to this factor or be due to the coarse categorical factor used to represent soil type (i.e. unmeasured edaphic variables may account for more variation). Edaphic variables are likely to be spatially structured, and we contend that this variation, if important, would be accounted for by the spatial component. Our results are in line with those of others who have observed that soil type and other soil chemical properties have a small but significant influence on fungal community composition (Verbruggen et al. 2012 for AM fungi).
By looking only at fungal subsets, we found Ascomycota showing the same strong phylogenetic pattern as total fungi, whereas for Glomeromycota and Basidiomycota, the spatial effect is more important than host plant phylogeny. Thus, our subgroups contributed differently to our variance partitioning pattern: Ascomycota mostly determined the large variance accounted for by host plant phylogeny, while Basidiomycota determined the fraction of variance accounted for soil type. All subgroups contributed more or less equally to the variance explained by space. The results of the subgroups have to be carefully interpreted because of potential differences in sampling depth among the subgroups. Glomeromycota for example represent only 2.4% of the sequences which may not be a representative sampling amount. The same might be true for Basidiomycota. Another aspect could be a real difference in host specificity, which is especially known for Glomeromycota (Smith & Read 2008). Studies on host specificity of Basidiomycota mostly addressed ECM fungi, which we may not have here.
Spatial position and soil type may be confounded by species identity because species usually were sampled in the same soil type and area. Because we were limited to sampling plants in the areas where they could be found and as their distributions were patchy, there was a degree of non-independence among the factors we tested. That said, this had little impact on the analysis as the variation was generally attributed to individual components and not to the dimensions in which multiple components overlapped. The results are therefore conservative as they accounted for possible sources of non-independence among the tested factors. Indeed, this is the strength of this study since we are studying these patterns under the natural conditions in which they occur.
It was not the aim of this study to test for effects of species identity on fungal community composition. However, we tested for this effect and results suggested that host species identity is a relatively poor predictor of fungal community relative to host plant phylogeny as it explains a similar proportion of variation but with substantially more costs associated with degrees of freedom (data not shown).
All in all, we could explain 30% of the variance with the three predictor variables (when correcting for co-linearity in sampling time), which indicates that assembly processes in root-associated fungal communities are predictable to a certain extent, at least in our system. Additional sources of variation could include, for example, neighbour effects or stochastic colonization followed by priority effects (Hausmann & Hawkes 2009; Mummey, Antunes & Rillig 2009; Dickie et al. 2012) or biotic interactions among the fungal species (Pan & May 2009). It was clear from the null model analysis of species co-occurrence that the root fungal communities (especially Ascomycota and Glomeromycota) were not random assemblages but demonstrated strong spatial segregation. This latter result suggests that negative interactions among fungi led to limiting similarity in their composition (Gotelli 2000) and indicate competition among fungi. Next to environmental filtering, which allows only certain fungi to persist (as shown in variance partitioning), these negative interactions could be an additional explanation for the pattern of community composition found in our study. These processes likely combine to contribute to the coexistence of similar species (Shigesada, Kawasaki & Teramoto 1979) and may thus help explain the observed level of fungal diversity in our system. Analysis of the Basidiomycota, however, suggested that interactions among these species had negligible effects on community assembly. Basidiomycota communities may have, therefore, been driven by environmental factors, but variance explained by our environmental factors was low. Further investigations might be necessary to explain the factors driving Basidiomycota community composition.