Assessing the impact of wildlife and livestock integration on soil microbial communities and pathogen prevalence (2017)
In the past decade, wildlife farming has demonstrated substantial growth in South Africa. The appeal of this farming practice is  primarily underpinned by its ability to serve as powerful economic tool,  capable of generating multiple streams of revenue  (e.g. game meat sales, trophy hunting and ecotourism). Despite the economic advantages of the approach, the potential for zoonotic disease transmission cannot be ignored. The poor understanding of disease transmission at the wildlife/livestock interface aggravates the issue as environmental conflicts between these populations threatens both animal/human health, and economic activities, where employed. Here, we aimed to delineate and compare environmental bacterial communities present within livestock, wildlife and contact (occupied by a combination of wildlife and livestock) scenarios, at  a commercial livestock farm and wildlife conservancy in Beaufort West, South Africa. A total of 12 samples were collected from wildlife (n=3), livestock (n =7) and contact (n=2) scenarios in the form of soil and dung. Following the extraction of DNA from samples, the 16S rRNA gene (V3-V4 hypervariable region) was targeted for amplification. Purified amplicons were sequenced using high-throughput Next Generation Sequencing (NGS) technologies. QIIME 1.9.1 was employed to the delineate differences in bacterial diversity between sampled scenarios, using a variety of diversity metrics (CHAO1, Observed OTUs, PD Whole tree, Shannon). Further assessments (ANOSIM, SIMPER) of bacterial communities for each of our sampled scenarios were performed using Primer V7. Pathogen screening was performed using the summary report provided by Illumina, in conjunction with the OTU tables generated by QIIME 1.9.1. ?-diversity analyses showed no differences in bacterial diversity between areas occupied by wildlife, livestock or mixed animal scenarios, with high levels of taxa overlap between samples. The highest contributing taxa identified by our assessments were the Firmicutes, which dominated livestock and mixed animal scenarios, while wildlife areas demonstrated higher levels of Planctomycetes and its members. Our analysis also led to the detection of five pathogens: E.albertii, E.lenta, C.gasigenes, L.acidophilus and E.sibiricum, which have the potential to incur health and economic losses at the farm, but are unlikely to pose an immediate threat due to low individual contributions to overall bacterial diversity.  Keywords:   bacteria, wildlife farming, disease transmission, pathogenesis, 16S rRNA