Soil Ph Determines Microbial Network Complexity and the Relative Abundance of Keystone Taxa Across Wheat Fields of the North China Plain

Abstract

There has been little study of the biogeographical patterns of microbial co-occurrence, especially in agricultural soils. Here we investigated the biogeographical patterns and major drivers of co-occurrence network topological structure, and the relative abundance of keystone taxa for soil bacterial and fungal communities using high-throughput sequencing on a set of 90 samples across a 1,092 Km transect in wheat field of the North China Plain (NCP). While an earlier study by Shi et al. divided the NCP into acidic and alkaline regions and explored the threshold effect of pH on microbial interaction structure respectively, we analysed the whole gradient as a continuum, finding that network properties varied as a continuum with pH, and that pH was the most important environmental factor in network topology and relative abundance of keystone taxa. For the metacommunity composed of both bacteria and fungi, and for the bacterial community alone, lower soil pH was associated with a more complex microbial network. However, the network for fungi showed no strong trend with soil pH. Dominant taxonomic taxa of bacteria and fungi could be grouped into ecological clusters according to pH preferences, with low-pH cluster taxa occupying a core position in the network and playing a more important role in maintaining the connectivity of the complex network, compared to taxa of the high-pH cluster. Unlike the trend in connectivity, associations within communities were most stable and dominated by negative associations at neutral pH. In addition, keystone taxa (e.g., Oxalobacteraceae, Burkholderiaceae and Mortierellaceae) were positively correlated with ecosystem function and stability, and relative abundance changes were statistically best explained by pH. Our results present new perspectives on impacts of pH on soil microbial network structure across large scales in agricultural environments. This improved knowledge of community processes provides a step towards understanding functioning and stability of agricultural ecosystems.

Keywords: Bacteria, Fungi, Co-occurrence network, Biogeographic model, Keystone taxa, soil pH