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Data from: A novel real-world ecotoxicological dataset of pelagic microbial community responses to wastewater

Cite this dataset

Ruprecht, Jamie et al. (2020). Data from: A novel real-world ecotoxicological dataset of pelagic microbial community responses to wastewater [Dataset]. Dryad.


Real-world observational datasets that record and quantify pressure-stressor-response linkages between effluent discharges and natural aquatic systems are rare.  With global wastewater volumes increasing at unprecedented rates, it is urgent that the present dataset is available to provide the necessary information about microbial community structure and functioning.  Field studies were performed at two time-points in the Austral summer.  Single-species and microbial community whole effluent toxicity (WET) testing was performed at a complete range of effluent concentrations and two salinities, with accompanying environmental data to provide new insights into nutrient and organic matter cycling, and to identify ecotoxicological tipping points.  The two salinity regimes were chosen to investigate future scenarios based on a predicted salinity increase at the study site, typical of coastal regions with rising sea levels globally.  Flow cytometry, amplicon sequencing of 16S and 18S rRNA genes and micro-fluidic quantitative polymerase-chain reactions (MFQPCR) were used to determine chlorophyll-a and total bacterial cell numbers and size, as well as taxonomic and functional diversity of pelagic microbial communities.  This strong pilot dataset could be replicated in other regions globally and would be of high value to scientists and engineers to support the next advances in microbial ecotoxicology, environmental biomonitoring and estuarine water quality modelling.


Refer to ReadMe files included with the dataset.

Usage notes

In this study, bacterial cell densities were quantified in the microbial WET tests from frozen samples several months after the initial flow cytometry was done using fresh samples.  Previous studies have reported decreases in bacterial (Kamiya et al., 2007) cell densities between natural and frozen samples.  Therefore, these data are recommended for assessment of relative differences between samples since they may reflect a slight underestimation of the bacterial relative to algal cell chlorophyll-a contributions.

While a number of studies have found that MFQPCR produces copy number estimates that are directly comparable to those produced with traditional qPCR (Ishii et al., 2014a, Byappanahalli et al., 2015), variations in reaction efficiency are common between samples from different sites (Brankatschk et al., 2012), and in MFQPCR these differences in efficiency may be substantial (Crane et al., 2018).  Therefore, whilst copy number tables presented in the data records are suitable for intra-study comparisons and modelling, it is recommended that the raw data files are utilised in studies intending to combine inter-study datasets, and uniform efficiency cut-offs be applied prior to further analysis.


Hunter Water, Award: UNSW Sydney RG160696

Australian Government Research Training Program Scholarship, Award: UNSW Sydney student ID z3187225