Core and conditionally rare taxa as indicators of agricultural drainage ditch and stream health and function

Abstract

Abstract Background The freshwater microbiome regulates aquatic ecological functionality, nutrient cycling, pathogenicity, and has the capacity to dissipate and regulate pollutants. Agricultural drainage ditches are ubiquitous in regions where field drainage is necessary for crop productivity, and as such, are first-line receptors of agricultural drainage and runoff. How bacterial communities in these systems respond to environmental and anthropogenic stressors are not well understood. In this study, we carried out a three year study in an agriculturally dominated river basin in eastern Ontario, Canada to explore the spatial and temporal dynamics of the core and conditionally rare taxa (CRT) of the instream bacterial communities using a 16S rRNA gene amplicon sequencing approach. Water samples were collected from nine stream and drainage ditch sites that represented the influence of a range of upstream land uses. Results The cross-site core and CRT accounted for 5.6% of the total number of amplicon sequence variants (ASVs), yet represented, on average, over 60% of the heterogeneity of the overall bacterial community; hence, well reflected the spatial and temporal microbial dynamics in the water courses. The contribution of core microbiome to the overall community heterogeneity represented the community stability across all sampling sites. CRT was primarily composed of functional taxa involved in nitrogen (N) cycling and was linked to nutrient loading, water levels, and flow, particularly in the smaller agricultural drainage ditches. Both the core and the CRT were sensitive responders to changes in hydrological conditions. Conclusions We demonstrate that core and CRT can be considered as holistic tools to explore the temporal and spatial variations of the aquatic microbial community and can be used as sensitive indicators of the health and function of agriculturally dominated water courses. This approach also reduces computational complexity in relation to analyzing the entire microbial community for such purposes.