Spatiotemporal distribution of environmental microbiota around animal farms adjacent to produce fields in central coast California

This study aimed to identify different environmental microbiota in animal farms adjacent to produce fields and to understand their potential flow pattern. Soil and water samples were collected from 16 locations during the winter, spring, summer, and fall seasons. In addition, a high-resolution digital elevation model helped to create a stream network to understand the potential flow of the microbiome. Metagenomic analysis of the 16 S rRNA gene revealed that soil and water samples from the four seasons harbor diverse microbiome profiles. The phylogenetic relationship of operational taxonomic units (OTUs) is separated by a maximum of 0.6 Bray-Curtis distance. Similarly, the Principal Component Analysis (P = 0.001) demonstrated the soil and water microbiome clustering across different locations and seasons. The relative abundance of Proteobacteria, Bacteroidetes, and Firmicutes was higher in the water samples than in the soil samples. In contrast, the relative abundance of Actinobacteria and Chloroflexi was higher in the soil compared to the water samples. Soil samples in summer and water samples in spring had the highest abundance of Bacteroidetes and Firmicutes, respectively. A unique microbial community structure was found in water samples, with an increased abundance of Hydrogenophaga and Solirubrobacter. Genera that were significantly abundant at a 1% false discovery rate (FDR) among seasons and soil or water samples, include Nocardioides, Gemmatimonas, JG30-KF-CM45, Massilia, Gaiellales, Sphingomonas, KD4-96, Bacillus, Streptomyces, Gaiella, and Gemmatimonadaceae. The relative abundance of pathogenic genera, including Mycobacterium, Bacteroides, Nocardia, Clostridium, and Corynebacterium, were significantly (at 1% FDR) affected by seasons and environmental type. The elevation-based stream network model suggests the potential flow of microbiomes from the animal farm to the produce fields.