16S and 18S rRNA Gene Metabarcoding Provide Congruent Information on the Responses of Sediment Communities to Eutrophication

Abstract

Metabarcoding analyses of bacterial and eukaryotic communities have been proposed as efficient tools for environmental impact assessment. It has been unclear, however, to which extent these analyses can provide similar or differing information on the ecological status of the environment. Here, we used 16S and 18S rRNA gene metabarcoding to compare eutrophication-induced shifts in sediment bacterial and eukaryotic community structure in relation to a range of porewater, sediment and bottom-water geochemical variables, using data obtained from six stations near a former rainbow trout farm in the Archipelago Sea (Baltic Sea). Shifts in the structure of both community types were correlated with a shared set of variables, including porewater ammonium concentrations and the sediment depth-integrated oxygen consumption rate. Distance-based redundancy analyses showed that variables typically employed in impact assessments, such as bottom water nutrient concentrations, explained less of the variance in community structure than alternative variables (e.g., porewater NH4+ inventories and sediment depth-integrated O2 consumption rates) selected due to their low collinearity (up to 40 vs. 58% of the variance explained, respectively). In monitoring surveys where analyses of both bacterial and eukaryotic communities may be impossible, either 16S or 18S rRNA gene metabarcoding can serve as reliable indicators of wider ecological impacts of eutrophication.

Evaluation of DNA extraction methods and direct PCR in metabarcoding of mock and marine bacterial communities

“…Concerning current advances in microbiome data analysis, despite rarefying still being a widely popular normalization technique in ecological studies, we implemented compositional approach in this research ( Fernandes et al, 2014 ; McMurdie and Holmes, 2014 ; Gloor et al, 2017 ). Compositional data analyses of NGS results are currently becoming more widespread with an emphasis that microbiome datasets can and should be treated as compositional ( Tsilimigras and Fodor, 2016 ; Gloor et al, 2017 ; Sisk-Hackworth and Kelley, 2020 ; Harrison et al, 2021 ).…”

A pile of pipelines: An overview of the bioinformatics software for metabarcoding data analyses

“…A variety of pipelines have been applied for the analyses of the amplicon sequences from these markers. For example, MICCA, DADA2 for 18S rRNA (Harrison et al, 2021;Minerovic et al, 2020); DADA2, OBITools for mt 16S (Marquina et al, 2019;Thomsen & Sigsgaard, 2019);…”

“…A variety of pipelines have been applied for the analyses of the amplicon sequences from these markers. For example, MICCA, DADA2 for 18S rRNA (Harrison et al, 2021; Minerovic et al, 2020); DADA2, OBITools for mt 16S (Marquina et al, 2019; Thomsen & Sigsgaard, 2019); and custom built pipelines (using multiple third‐party sequence data analysis tools) for other markers above (Anslan et al, 2021; Elbrecht et al, 2016; Liu & Zhang, 2021; Westfall et al, 2020). Benchmarked on mt 12S reads from both simulated and real eDNA data, the Barque pipeline demonstrated a small sensitivity improvement over QIIME 2 and OBITools (Mathon et al, 2021).…”

Evaluating eDNA for Use within Marine Environmental Impact Assessments

“…A promising new use of eDNA in EIA is for characterizing changes in community structures [31,[137][138][139][140]; however, community-level changes are detected based on exploratory statistics (multidimensional data analysis) rather than inference. Interestingly, even if it does not rest on a strict application of decision theory, Kelly et al [141] explore inferences based on rarefaction curve and correlation with environmental properties for different levels of diversity along a gradient of disturbances in Puget Sound, Washington, USA.…”