TICI is a taxon-independent community index for eDNA-based ecological health assessment. The study developed a riverine taxon-independent community index (TICI) that objectively assigns indicator values to amplicon sequence variants (ASVs), significantly improving the statistical power and utility of eDNA-based bioassessments. The TICI model training step uses the Chessman iterative learning algorithm to assign health indicator scores to a large number of ASVs that are commonly encountered across a wide geographic range. New sites can then be evaluated for ecological health by averaging the indicator value of the ASVs present at the site. The TICI model was trained on an eDNA dataset from 53 well-studied riverine monitoring sites across New Zealand, each sampled with a high level of biological replication (n=16). Eight short-amplicon metabarcoding assays were used to generate data from a broad taxonomic range, including bacteria, microeukaryotes, fungi, plants, and animals. Site-specific TICI scores were strongly correlated with historical stream condition scores from macroinvertebrate assessments (macroinvertebrate community index or MCI; R²=0.82), and TICI variation between sample replicates was minimal (CV=0.013). Taken together, this demonstrates the potential for taxon-independent eDNA analysis to provide a reliable, robust and low-cost assessment of ecological health that is accessible to environmental managers, decision makers, and the wider community. The study shows that the TICI index provides a precise and repeatable measure of ecological health that could be extended to other environments. The TICI index is a precise, accurate and cost-effective ecological condition indicator that makes thorough use of both assignable and unassignable eDNA metabarcoding output. The TICI index offers high precision as demonstrated by low variance in scores between replicate samples. This feature was particularly evident in the 98% of samples for which at least 100 unique indicator ASVs were detected. The distribution of indicator scores within samples were distributed as non-skewed normal distributions (see Fig. 4), with the TICI index derivable from the mean parameter without bias. A useful feature of this precision is that it can make use of samples collected with lower levels of replication than needed in taxon-dependent eDNA metabarcoding studies. The study also shows that the TICI index can be used as a reliable indicator to help address basic questions about the presence or absence of culturally-important species, the current state of the waterway, and whether it is improving or deteriorating. The TICI index is a valuable tool for environmental monitoring and management, providing a reliable, robust and low-cost assessment of ecological health that is accessible to environmental managers, decision makers, and the wider community.TICI is a taxon-independent community index for eDNA-based ecological health assessment. The study developed a riverine taxon-independent community index (TICI) that objectively assigns indicator values to amplicon sequence variants (ASVs), significantly improving the statistical power and utility of eDNA-based bioassessments. The TICI model training step uses the Chessman iterative learning algorithm to assign health indicator scores to a large number of ASVs that are commonly encountered across a wide geographic range. New sites can then be evaluated for ecological health by averaging the indicator value of the ASVs present at the site. The TICI model was trained on an eDNA dataset from 53 well-studied riverine monitoring sites across New Zealand, each sampled with a high level of biological replication (n=16). Eight short-amplicon metabarcoding assays were used to generate data from a broad taxonomic range, including bacteria, microeukaryotes, fungi, plants, and animals. Site-specific TICI scores were strongly correlated with historical stream condition scores from macroinvertebrate assessments (macroinvertebrate community index or MCI; R²=0.82), and TICI variation between sample replicates was minimal (CV=0.013). Taken together, this demonstrates the potential for taxon-independent eDNA analysis to provide a reliable, robust and low-cost assessment of ecological health that is accessible to environmental managers, decision makers, and the wider community. The study shows that the TICI index provides a precise and repeatable measure of ecological health that could be extended to other environments. The TICI index is a precise, accurate and cost-effective ecological condition indicator that makes thorough use of both assignable and unassignable eDNA metabarcoding output. The TICI index offers high precision as demonstrated by low variance in scores between replicate samples. This feature was particularly evident in the 98% of samples for which at least 100 unique indicator ASVs were detected. The distribution of indicator scores within samples were distributed as non-skewed normal distributions (see Fig. 4), with the TICI index derivable from the mean parameter without bias. A useful feature of this precision is that it can make use of samples collected with lower levels of replication than needed in taxon-dependent eDNA metabarcoding studies. The study also shows that the TICI index can be used as a reliable indicator to help address basic questions about the presence or absence of culturally-important species, the current state of the waterway, and whether it is improving or deteriorating. The TICI index is a valuable tool for environmental monitoring and management, providing a reliable, robust and low-cost assessment of ecological health that is accessible to environmental managers, decision makers, and the wider community.