RESULTS: Here we combine quantitative microbiome profiling (QMP; parallelization of amplicon sequencing and 16S rRNA qPCR to estimate cell counts) and absolute resistome profiling (based on high-throughput qPCR) to quantify AR along an anthropogenically impacted river. We show QMP overcomes biases caused by relative taxa abundance data and show the benefits of using unified Hill number diversities to describe environmental microbial communities. Our approach overcomes weaknesses in previous methods and shows Hill numbers are better for QMP in diversity characterisation.
CONCLUSIONS: Methods here can be adapted for any microbiome and resistome research question, but especially providing more quantitative data for QMRA and other environmental applications.
CONCLUSION: The identification of a core microbiome furthers our understanding of mangrove microbial biodiversity, particularly in Southeast Asia where studies such as this are rare. The identification of significantly different microbial communities between sampling sites suggests environmental filtering is occurring, with hosts selecting for a microbial consortia most suitable for survival in their immediate environment. As climate change advances, many of these microbial communities are predicted to change, however, without knowing what is currently there, it is impossible to determine the magnitude of any deviations. This work provides an important baseline against which change in microbial community can be measured.