PRJNA505831
Metabarcoding of sample preservative versus morphometric identification of macroinvertebrates to assess stream impacts from forest harvesting
Aquatic macroinvertebrate communities are often used to assess the ecological integrity of streams. However, conventional methods to assess macroinvertebrate communities are usually costly and time-consuming. Here we compare stream macroinvertebrate community metrics based on conventional morphometrics vs. non-destructive DNA metabarcoding from storage ethanol to assess forest management impacts on headwater streams in an intensively managed forest watershed in eastern Canada. The two approaches demonstrated substantial congruence in the detection of taxa (81% and 69% at the family and genus level, respectively) and in the characterization of community composition and richness. However, DNA metabarcoding from preservative ethanol identified significantly less genera (3.3 on average) and families (2.0) than conventional morphometrics. Taxa missed by metabarcoding from ethanol were typically low in proportional mass or poorly represented in the CO1 reference database. This led to some differences in the explanatory variables identified as being related to macroinvertebrate metrics, which could have implications on conclusions and management actions that might result therefrom. For example, the negative relationships between reach variables associated with forest management intensity and richness identified based on morphometrics were weaker based on metabarcoding. Discriminatory power was greater when data at the genus level were used. The congruence between functional feeding group results derived from morphometric (based on relative abundance) vs. metabarcoding (based on relative frequency and read abundance) identifications was group specific (r = 0.16-0.63), but low overall. We conclude that DNA metabarcoding of storage ethanol provides a promising approach for generating biomonitoring data, but that its full deployment in biomonitoring projects requires developing more complete reference libraries and enhancing the sensitivity for detecting taxa with low sample biomass.