Implications of taxonomic and numerical resolution on DNA metabarcoding-based inference of benthic macroinvertebrate responses to river restoration

Exploring and clearly defining the level of taxonomic identification and quantification approaches for diversity and biomonitoring studies are essential, given its potential influence on the assessment and interpretation of ecological outcomes. In this study, we evaluated the response of benthic macroinvertebrate communities to the restoration or construction of gravel bars conducted in the dam-impacted Trinity River, with the non-dam influenced tributaries serving as the reference sites. We aim to evaluate the performance of different taxonomic and numerical (i.e., abundance vs. presence/absence data) resolutions of DNA metabarcoding with consequent comparison to morphology-based identification and how it affects assessment outcomes. DNA metabarcoding detected 93% of the morphologically identified individuals and provided finer taxonomic resolution. We also detected significant correlations between morphological sample abundance, biomass, and DNA metabarcoding read abundance. We observed a relatively high and significant congruence in macroinvertebrate community structure and composition between different taxonomic and numerical resolutions of both methods, indicating a satisfactory surrogacy between the two approaches and their varying identification levels and data transformation. Additionally, the community-environmental association were significant for all datasets but showed varying significant associations against the physicochemical parameters. Furthermore, both methods identified Simulium spp. as significant indicators of the dam-impacted gravel bars. Still, only DNA metabarcoding showed significant false discovery rate proving the methods robustness compared to morphology-based identification. Our observations imply that coarser taxonomic resolution could be highly advantageous to DNA metabarcoding-based applications in situations where the lack of taxonomic information, e.g., poor reference database, might severely affect the quality of biological assessments.