Environmental DNA (eDNA) extracted from water is routinely used in river biodiversity research, and via metabarcoding eDNA can provide comprehensive taxa lists with little effort and cost. However, eDNA-based species detection in streams and rivers may be influenced by sampling season and other key factors such as water temperature and discharge. Research linking these factors and also informing on the potential of eDNA metabarcoding to detect shifts in ecological signatures, such as species phenology and functional feeding groups across seasons, is missing. To address this gap, we collected water samples every two weeks for 15 months at a long-term ecological research (LTER) site and at three different positions in the river’s cross section, specifically the water surface, riverbed, and riverbank. For these 102 samples, we analyzed macroinvertebrate species and molecular Operational Taxonomic Unit (OTU) richness and temporal community turnover across seasons based on COI metabarcoding data. Using Generalized Additive Models, we found a significant influence of sampling season on species richness. Community turnover followed a cyclic pattern, reflecting the continuous change of the macroinvertebrate community throughout the year (‘seasonal clock’). Although water temperature had no influence on the inferred species richness, higher discharge reduced the number of Annelida and Ephemeroptera species detectable with eDNA. Most macroinvertebrate taxa showed the highest species richness in spring, in particular merolimnic species with univoltine life cycles. Further, we detected an increase in proportion of shredders in winter and of parasites in summer. Our results show the usefulness of highly resolved eDNA metabarcoding time series data for ecological research and biodiversity monitoring in streams and rivers.

Environmental DNA (eDNA) metabarcoding has gained growing attention as a strategy for monitoring biodiversity in ecology. However, taxa identifications produced through metabarcoding require sophisticated processing of high-throughput sequencing data from taxonomically informative DNA barcodes. Various sets of universal and taxon-specific primers have been developed, extending the usability of metabarcoding across archaea, bacteria, and eukaryotes. Accordingly, a multitude of metabarcoding data analysis tools and pipelines have also been developed. Often, several developed workflows are designed to process the same amplicon sequencing data, making it somewhat puzzling to choose one amongst the plethora of existing pipelines. However, each pipeline has its own specific philosophy, strengths, and limitations, which should be considered depending on the aims of any specific study, as well as the bioinformatics expertise of the user. In this review, we outline the input data requirements, supported operating systems, and particular attributes of thirty-one amplicon processing pipelines with the goal of helping users to select a pipeline for their metabarcoding projects.

Environmental DNA (eDNA) extracted from water is routinely used in river biodiversity research, and via metabarcoding eDNA can provide comprehensive taxa lists with little effort and cost. However, eDNA-based species detection in streams and rivers may be influenced by sampling season, location, and other key factors such as water temperature and discharge. Research linking these factors and also informing on the potential of eDNA metabarcoding to detect shifts in ecological signatures, such as species phenology and functional feeding groups across seasons, is missing. To address this gap, we collected 102 water samples every two weeks for 15 months at a long-term ecological research (LTER) site and at three different positions in the river’s cross section, specifically the water surface, riverbed, and riverbank. We analyzed macroinvertebrate species and molecular Operational Taxonomic Unit (OTU) richness and temporal community turnover across seasons and sampling positions based on COI metabarcoding data. Using Generalized Additive Models, we found a significant influence of sampling season but not sampling position on community composition. Community turnover followed a cyclic pattern, reflecting the continuous change of the macroinvertebrate community throughout the year (‘seasonal clock’). Although water temperature had no influence on the inferred community composition, higher discharge reduced the number of Annelida and Ephemeroptera species detectable with eDNA. Most macroinvertebrate taxa showed the highest detection rates in spring, in particular merolimnic species with univoltine life cycles. Further, we detected an increase in proportion of shredders in winter and of parasites in summer. Our results show the usefulness of highly resolved eDNA metabarcoding time series data for ecological research and biodiversity monitoring in streams and rivers.