Comparison of markers for the monitoring of freshwater benthic biodiversity through DNA metabarcoding

Abstract

Metabarcoding of bulk or environmental DNA has great potential for biomonitoring freshwater environments. However, successful application of metabarcoding to biodiversity monitoring requires universal primers with high taxonomic coverage that amplify highly variable, short metabarcodes with high taxonomic resolution. Moreover, reliable and extensive reference databases are essential to match the outcome of metabarcoding analyses with the available taxonomy and biomonitoring indices. Benthic invertebrates, particularly insects, are key taxa for freshwater bioassessment. Nevertheless, few studies have so far assessed markers for metabarcoding of freshwater macrobenthos. Here we combined in silico and laboratory analyses to test the performance of different markers amplifying regions in the 18S rDNA (Euka02), 16S rDNA (Inse01), and COI (BF1_BR2-COI) genes, and developed an extensive database of benthic macroinvertebrates of France and Europe, with a special focus on key insect orders (Ephemeroptera, Plecoptera and Trichoptera). Analyses on 1514 individuals representing different taxa of benthic macroinvertebrates showed very different amplification success across primer combinations. The Euka02 marker showed the highest universality, while the Inse01 marker showed excellent performance for the amplification of insects. BF1_BR2-COI showed the highest resolution, while the resolution of Euka02 was often limited. By combining our data with GenBank information, we developed a curated database including sequences representing 822 genera. The heterogeneous performance of the different primers highlights the complexity of the identification of the best markers, and advocates for the integration of multiple metabarcodes for a more comprehensive and accurate understanding of ecological impacts on freshwater biodiversity.

Validating eDNA measurements of the richness and abundance of anurans at a large scale

“…The laboratory work (DNA extraction, PCR, high‐throughput sequencing and data analysis) associated with metabarcoding can be expensive; however, the total cost of metabarcoding is substantially lower than that of the TLTM (Table S21). These results are in agreement with increasing evidence that eDNA metabarcoding is a more economically efficient tool for large scale biodiversity monitoring than traditional methods (Ficetola et al., 2020).…”

“…Although the TLTM is recommended as one of the best conventional methods for rapid assessment of amphibian diversity (Rödel & Ernst, 2004), both richness and abundance estimated by the TLTM vary considerably, depending on a habitat type, habitat complexity, ecology and behaviour of a species, prevailing weather conditions, season, and experience of a researcher (Branch & Rodel, 2003; Jaeger, 1994; Ribeiro‐Júnior et al., 2008; Rödel & Ernst, 2004). Second, eDNA methods generally have lower rates of false negatives than that obtained by traditional methods (Ficetola et al., 2020). Metabarcoding detected the occurrence of each anuran species that had been identified by TLTM in each waterbody (Table S18), indicating that the rates of the false negatives derived by metabarcoding are likely to be negligible at the waterbody level.…”

“…Major studies have focused on the reliability of measurements of fish abundance in naturally aquatic environments by metabarcoding (Deiner et al. 2017; Thomsen & Willerslev, 2015; Yates et al., 2019); however, relatively low attention has been paid to amphibians (Ficetola et al., 2020; McKee et al., 2015). The variations in relative read count across waterbodies explained by density vary in anurans in Liuheng.…”

“…Both reliability and cost need to be considered during the selection of the methods for biodiversity monitoring (Balint et al., 2018; Ficetola et al., 2020). Many factors, including number of sampling species and sites, costs of field survey, molecular analyses and replicates, can affect the efficiency of metabarcoding versus that of the traditional methods (Balint et al., 2018; Davy et al., 2015; Harper et al., 2018; Smart et al., 2016).…”

Ethanol eDNA Reveals Unique Community Composition of Aquatic Macroinvertebrates Compared to Bulk Tissue Metabarcoding in a Biomonitoring Sampling Scheme

“…In addition to these biological unknowns surrounding the transfer of eDNA to ethanol, there are also technical biases which can also result in the discrepancy between community composition between our sample methods (see Reference [38] for summary). For example, the choice of DNA extraction method can result in biases in community data [41]. We used two different extraction methods here (Qiagen DNEasy kits for tissue and a CTAB buffer extraction for eDNA) to maximize respective yield and quality; however, this may have introduced some of the differences we observed in our dataset.…”