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.

Figure 1

Autocorrelation (Semivariogram) and trends in environmental variables create (arrow a) spatial structure and environmental gradients. Variation in the environment generates variation in plants and AMF (arrows b). AMF and plants can thus be structured by changes in habitat conditions, which can then simply lead to covariation between the two assemblages (Habitat hypothesis). Alternatively, AMF could either drive the plant assemblage (Driver hypothesis, arrow c) or be driven by the plant assemblage (Passenger hypothesis, arrow d). In all cases, the driving factors/assemblage (b–d) have a spatial structure that will be, at least partially, reflected by spatial structure in the driven assemblage. This spatial dependence calls for a spatially explicit approach to the testing of the three hypotheses. Spatial scale and successional stage have also been hypothesized to be the major factors in determining which among the Habitat, Driver and Passenger hypotheses apply to real systems. In addition to all these factors, AMF can also be structured by interactions within the assemblage, independently of plants, which has been hypothesized to happen at local scale and that could create very patchy distribution. All data are simulated.

Figure 2

Kriging interpolation of four of the measured environmental variables as measured in one of the three macroplots (macroplot 1, see Supplementary Information). Plots were by construction aligned along a soil textural gradient on the slopes of a hillside (Supplementary Figure S1), with the gradient running along the uphill–downhill axis (y axis; Supplementary Figures S2 and S3). As we expected, the main gradient in major soil variables followed the uphill–downhill axis, although in the case of macroplot 1 water showed a patchy distribution.

Figure 3

Kriging interpolation of the first two PCoA (see also Figure 4) axes of AMF and plants. Data are shown for macroplot 1, and are thus directly comparable with environmental variables presented in Figure 2. Spatial patterns in the structure of the two assemblages appear to be only poorly correlated. Similar patterns were observed in the other macroplots (not shown).

Figure 4

PCoA ordination plots of plants and AMF. Individual samples are color labeled by macroplot (M1, blues; M2, red; M3, black) and symbol label in terms of uphill (up, triangle) or downhill (down, square) position of individual samples within the macroplot (see also Supplementary Figure S1). The plant assemblage appears to be more spatially structured in terms of the separation between M3 and M2+M1, with the latter two being geographically much closer to each other (Supplementary Figure S2). This clustering pattern is less evident in AMF.

Figure 5

Bivariate covariation of PCoA2 and 2 of both AMF (roots) and plants (see Figure 4) in all four possible combinations: (a) PCoA1 AMF vs PCoA1 plants; (b) PCoA2 AMF vs PCoA1 plants; (c) PCoA1 AMF vs PCoA2 plants; (d) PCoA2 AMF vs PCoA2 plants. Pearson's correlation coefficient (r) and relative P-value (P) is reported for each set of correlations. Individual samples are color labeled by macroplot (M1, blues; M2, red; M3, black). Some significant correlation is observed but seems driven by spatial structure between macroplots. For example, in a and c, M3 samples are clustered on the right-hand side, while in d, the observed positive correlation between the PCoA2 axes of plants and AMF is driven by variation internal to macroplot 1. These results suggest spatial dependence in the covariation between AMF and plants.