The Community Structures of Prokaryotes and Fungi in Mountain Pasture Soils are Highly Correlated and Primarily Influenced by pH

Abstract

Traditionally, conservation and management of mountain pastures has been managed solely on the basis of visible biota. However, microorganisms play a vital role for the functioning of the soil ecosystem and, hence, pasture sustainability. Here, we studied the links between soil microbial (belowground) community structure (using amplicon sequencing of prokaryotes and fungi), other soil physicochemical and biological properties and, finally, a variety of pasture management practices. To this aim, during two consecutive years, we studied 104 environmental sites characterized by contrasting elevation, habitats, bedrock, and pasture management; located in or near Gorbeia Natural Park (Basque Country/Spain). Soil pH was found to be one of the most important factors in structuring soil microbial diversity. Interestingly, we observed a striking correlation between prokaryotic, fungal and macrofauna diversity, likely caused by interactions between these life forms. Further studies are needed to better understand such interactions and target the influence of different management practices on the soil microbial community, in face of the significant heterogeneity present. However, clearing of bushes altered microbial community structure, and in sites with calcareous bedrock also the use of herbicide vs. mechanical clearing of ferns.

Keywords: belowground interactions; biodiversity; grassland soil; microbial diversity; pasture management; soil microbial communities; soil properties.

Figure 1

Simplified theoretical meta-model of the grassland ecosystem studied. Causal links are illustrated by arrows and questions associated to particular links indicated.

Figure 2

Comparison of prokaryotic (16S) vs. fungal (ITS) rarefied richness estimates between samples. Valley samples are symbolized by circles, low mountain by squares and high mountain by triangles. Samples with siliceous bedrock are colored black while those from calcareous are colored gray.

Figure 3

Barplots representing the distribution of the 30 most abundant prokaryotic taxa (A; family level or below) and the 20 most abundant fungal taxa (B; order level or below).

Figure 4

Non-metric multidimensional scaling (NMDS) based on Bray-Curtis dissimilarities of community composition. Composition was based on relative OTU abundances from (A) prokaryotic 16S and (B) fungal ITS amplicon data. Sites are labeled according to legend and the area containing all mountain sites is enclosed by blue lines. Red vectors indicate fitted environmental parameters significantly correlated to NMDS coordinates.

Figure 5

Structural Equation Model (SEM) derived from the theoretical meta-model of the community (see Figure 1) and corresponding questions targeted. Latent variables are illustrated as ellipses, exogenous variables are as boxes and theoretical constructs as boxes with dashed lines. Causal links indicated as significant in the total or group-wise (M only) best-fitting maximum likelihood solution to the model (see Table S4) are indicated in black while others are indicated as gray. Positive or negative correlations are indicated by ± signs and when differing between the fit for the total dataset and M both are indicated.

Figure 6

Kendall rank-correlation network of taxa based on relative abundances across samples. Taxa are represented by circles and colored according to taxonomic identify (see legend). Circle size is proportional to average abundance across datasets (cut-off for inclusion = 0.01%) and thickness of edges to strength of correlation (cut-off for inclusion: |τ| > 0.8). Significant correlation to environmental parameters is annotated.

Figure 7

Taxon abundances showing significant differences in sites cleared from bushes 1, 3 and 5 years prior to first sampling. Width of notches indicates 95% confidence intervals of median relative abundance. p-values determined by group-wise ANOVA (and verified by Tukey's range test) are given below each boxplot.