University of Calgary; Bamfield Marine Sciences Centre, Canada
Background/Question/Methods
The use of environmental DNA (eDNA) to quantify aquatic biodiversity is a rapidly emerging technique used for academic, government, and private purposes. The ease and relative low costs of taking environmental samples versus other methods of sampling makes eDNA methods an attractive choice, however, the broad variability in natural environments and a lack of standardization in laboratory analysis methods means that the robustness and replicability of these methods are difficult to assess. To address these deficiencies, we conducted a controlled winter experiment using 12 naturalized experimental streams and stocked caged Brook trout (Salvelinus fontinalis), Rainbow Trout (Oncorhynchus mykiss), and Cutthroat Trout (Oncorhynchus clarkii) in each stream at varying biomasses. Triplicate water samples were collected from the start, middle, and end of each stream and filtered to collect DNA. We analyzed these samples using broad-target DNA metabarcodes, narrow-target DNA metabarcodes, and species-specific qPCR to compare the detection rate and quantification reliability of each method.
Results/Conclusions
Our study showed that both assay choice and bioinformatic choices affected species detection and taxon resolution. DNA quantities varied with biomass and distance downstream, consistent with previous research, but also with an an ecological gradient present in the naturalized streams. Our results indicate that stream ecology is crucial to take into consideration when making inferences from eDNA methods. We also discuss the importance of accounting for overdispersion and non-detects that frequently arise in eDNA data due to the physical state of eDNA in aquatic environments. To our knowledge, this is the first eDNA experiment to directly compare DNA metabarcoding and qPCR under a controlled, replicated design within a semi-natural system. This work highlights the need for development of process-based models when using eDNA application to infer species abundances.
