eDNA metabarcoding is an emerging tool that is increasingly utilized for comprehensive biodiversity assessments across systems and applications. A growing number of ecological inferences and management decisions are likely to rely on data resulting from this approach. However, there is a lack of standards and best practices, and important bioinformatic and analytical decisions are often underreported. The absence of comparative studies exploring the impacts of these key decisions prevents a holistic view of the efficacy and utility of eDNA Metabarcoding. These deficiencies impede data transparency, exacerbate the reproducibility crisis, and undermine the potential of eDNA metabarcoding to enable large-scale biodiversity observations across systems. In this study, we examined how a key taxonomic assignment decision–namely the percent matching threshold for taxonomic assignment–impacts taxonomic richness, biodiversity observations and other ecological inferences. We utilized a temporally replicated metabarcoding dataset provided by the National Ecological Observatory Network (NEON) that captures spatial and temporal variation in macroinvertebrate communities of wadeable streams from Puerto Rico to Alaska. We evaluated differences in raw species richness and diversity metrics at key levels of taxonomic organization, from order to sub-species level genetic variants (i.e., exact sequence variants, ESVs). Additionally, we compared multivariate community analyses across taxonomic assignment thresholds to determine how these bioinformatic decisions impacted our ability to assess differences among streams through space and time. Results from this study will be widely applicable to biodiversity research that uses eDNA metabarcoding data to assign species based on the degree of match to reference databases. Understanding the impacts of bioinformatic decisions on taxonomic assignment may have critical implications for biodiversity observation and subsequent ecological inferences and management decisions.