Composition of the microbial communities in the mineral soil under different types of natural forest

TLDR: In this article, the composition of soil microbial communities under 12 natural forest stands including oak and beech, spruce-fir-beech, floodplain and pine forests were quantified by substrate-induced respiration and chloroform fumigation-extraction.

Abstract: Phospholipid fatty acid (PLFA) patterns were used to describe the composition of the soil microbial communities under 12 natural forest stands including oak and beech, spruce–fir–beech, floodplain and pine forests. In addition to the quantification of total PLFAs, soil microbial biomass was measured by substrate-induced respiration and chloroform fumigation–extraction. The forest stands possess natural vegetation, representing an expression of the natural site factors, and we hypothesised that each forest type would support a specific soil microbial community. Principal component analysis (PCA) of PLFA patterns revealed that the microbial communities were compositionally distinct in the floodplain and pine forests, comprising azonal forest types, and were more similar in the oak, beech and spruce–fir–beech forests, which represent the zonal vegetation types of the region. In the nutrient-rich floodplain forests, the fatty acids 16:1u5, 17:0cy, a15:0 and a17:0 were the most prevalent and soil pH seemed to be responsible for the discrimination of the soil microbial communities against those of the zonal forest types. The pine forest soils were set apart from the other forest soils by a higher abundance of PLFA 18:2u6,9, which is typical of fungi and may also indicate ectomycorrhizal fungi associated with pine trees, and high amounts of PLFA 10Me18:0, which is common in actinomycetes. These findings suggest that the occurrence of azonal forest types at sites with specific soil conditions is accompanied by the development of specific soil microbial communities. The study provides information on the microbial communities in undisturbed forest soils which may facilitate interpretation of data derived from managed or even damaged or degraded forests. q 2004 Elsevier Ltd. All rights reserved.