Soil Fungal Community in Grazed Inner Mongolian Grassland Adjacent to Coal-Mining Activity

Abstract

Coal mining results in reduced soil quality and makes environments less stable. Soil fungi are suitable indicators of soil quality for monitoring purposes. Here, the objective was therefore to investigate the effects of grazing and mining on the composition of the soil fungal community at the periphery of an opencast coal-mine dump in the Shengli mining area, Xilingol League, Inner Mongolia. A total of 2,110 fungal operational taxonomic units were identified and subdivided into 81 orders and nine categories, based on trophic modes. The sensitive factor to mining was soil pH, and that to grazing were soil nitrate-nitrogen and alkaline phosphatase activity. According to the Pearson correlation and Mantel test, we propose interactions between grazing and coal-mining exist a co-effect and could regulate edaphic variables to alter the behavior of soil fungal community. Moreover, compared with coal-mining, grazing has a greater impact on it. The results provide a basis to further clarify soil fungal ecological functions, and may also contribute to the practice of soil remediation and environmental management in coal-mining areas.

Keywords: Stipa krylovii; coal mining; edaphic variables; fungal community; grazing.

FIGURE 1

Operational taxonomic unit (OTU) Venn diagram of fungal communities in different routes and at different distances. AR, route (A); BR, route (B); ER, route (E).

FIGURE 2

Differences in fungal abundance-based coverage estimate (ACE), number of observed species (Sobs), phylogenetic diversity (PD) and Shannon–Wiener index at different distances and routes. Two-way ANOVA reveals the effect of route (R), distance (D), and their interactions (R × D) on fungal diversity indices (ns, not significant, P ≥ 0.05; ***P < 0.001). Error bars represent standard deviation. Bars followed by lowercase letters are significantly different among distances and routes (P < 0.05).

FIGURE 3

Non-metric multidimensional scaling (NMDS) of fungal community (stress = 0.249). Significant variables NO₃^– –N (NO₃^– –N; R² = 0.430, P < 0.001), alkaline phosphatase (ALP; R² = 0.419, P < 0.001), acid phosphatase (ACP; R² = 0.204, P = 0.026), electrical conductivity (EC, R² = 0.180, P = 0.034) and pH (R² = 0.242, P = 0.012) are fitted onto the NMDS graph based on the results of “envfit” function analysis. The arrows represent fitted vectors of edaphic variables with distribution significantly correlated with fungal community composition (*P < 0.05, ***P < 0.001). SOM, soil organic matter content; AK, available potassium; AP, available P; Urease, activity of soil urease.

FIGURE 4

Relationships among distances, routes, edaphic variables, fungal diversity indices, and composition of fungal community. Pairwise comparisons of edaphic variables and fungal diversity indices with a color gradient denoting Pearson’s correlation coefficient (*P < 0.05, **P < 0.01, ***P < 0.001). Distances, routes, and fungal community composition were related to edaphic variables and fungal diversity indices by Mantel test. SOM, soil organic matter content; EC, electrical conductivity; AK, available potassium; AP, available P; Urease, activity of soil urease; ALP, activity of alkaline phosphatase; ACP, activity of acid phosphatase; Shannon, Shannon–Wiener index; PD, phylogenetic diversity.

FIGURE 5

Indicator fungi with LDA scores of 3.5 or greater in fungal communities associated with different distances (A) and routes (B).