Composition of soil fungal communities and microbial activity along an elevational gradient in Mt. Jiri, Republic of Korea

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Approximately 64% of the Republic of Korea comprises mountainous areas, which as cold and high-altitude regions are gravely affected by climate change. Within the mountainous and the alpine-subalpine ecosystems, microbial communities play a pivotal role in biogeochemical cycling and partly regulate climate change through such cycles. We investigated the composition and function of microbial communities, with a focus on fungal communities, in Republic of Korea’s second tallest mountain, Mt. Jiri, along a four-point-altitude gradient: 600-, 1,000-, 1,200-, and 1,400-m. Soil pH and elevation were negatively correlated, with soils becoming more acidic at higher altitude. Of the five soil enzyme activities analyzed, cellobiohydrolase, β-1,4-glucosidase, and β-1,4-xylosidase activity showed differences among the elevation levels, with lower activity at 600 m than that at 1,400 m. Soil microbial biomass correlated positively with increasing elevation and soil water content. The decrease in β-1,4-N-acetylglucosaminidase suggests a reduction in fungal biomass with increasing altitude, while factors other than elevation may influence the increase in activity of the cellobiohydrolase, β-1,4-glucosidase and β-1,4-xylosidase. Fungal alpha diversity did not exhibit an elevational trend, whereas beta diversity formed two clusters (600–1,000 m and 1,200–1,400 m). Community composition was similar among the elevations, with Basidiomycota being the most predominant phylum, followed by Ascomycota. Conversely, among the fungal communities at 1,000 m, Ascomycota was the most dominant, possibly due to increased pathotroph percentage. Elevational gradients induce changes in soil properties, vegetation, and climate factors such as temperature and precipitation, all of which impact soil microbial communities and altogether create a mutually reinforcing system. Hence, inspection of elevation-based microbial communities can aid in inferring ecosystem properties, specifically those related to nutrient cycling, and can partly help assess the oncoming direct and indirect effects of climate change.