# Dataset on soil microbial community composition based on 16S rRNA gene OTUs from estuarine wetlands in China

## TL;DR

This paper presents a dataset of soil microbial communities from estuarine wetlands in China, including bacterial and fungal OTUs and network analyses.

## Contribution

The study provides a comprehensive dataset of microbial community composition and molecular ecological networks from nine Chinese estuarine wetlands.

## Key findings

- The dataset includes bacterial and fungal OTU tables from 29 soil samples across nine estuarine wetlands in China.
- Molecular ecological networks and network topology indices were generated using the MENAP pipeline and statistical thresholds.

## Abstract

This dataset comprises molecular ecological networks (MENs) and associated microbial community data from surface soils (0–5 cm depth) across nine Chinese estuarine wetlands: Liaohe Estuary (Liaoning province), Haihe Estuary (Tianjin Municipality), Yellow River Estuary (Shandong province), Sheyanghe Estuary (Jiangsu province), Changjiang Estuary (Shanghai Municipality), Oujiang Estuary (Zhejiang province), Jiulongjiang Estuary (Fujian province), Zhujiang Estuary (Guangdong province), and Beibuwan Gulf (Guangxi province). Twenty-nine composite samples (15–20 subsamples/site) were collected during August-September 2019.

Data include: Bacterial (16S V4-V5) and fungal (ITS1) OTU tables clustered at 97 % similarity (SILVA/UNITE databases; NCBI SRA PRJNA755846); Global and regional MEN adjacency matrices (Northern/Eastern/Southern China Sea groupings); Network topology indices (node/edge counts, average degree, modularity); Node-level metrics (betweenness centrality, clustering coefficient); Identified "Broker'' OTUs based on network indexes for individual nodes.

MENs were constructed using the Molecular Ecological Network Analysis Pipeline (MENAP, http://ieg2.ou.edu/MENA/) with Pearson correlations of log-transformed OTU abundances. Only OTUs present in >50 % of samples per group were included, with significance thresholds determined via Random Matrix Theory.