TICI: a taxon-independent community index for eDNA-based ecological health assessment. TICI: a taxon-independent community index for eDNA-based ecological health assessment

Global biodiversity is declining at the fastest rate that humans have experienced, and the rate of decline is increasing. Policies that might lead to mitigation or reversal of this process require ecosystem condition monitoring data that is rarely available. Morphology-based bioassessment methods tend to be limited in scope, suffer prohibitive costs, require skilled taxonomists, and can be applied inconsistently applied between practitioners. Environmental DNA (eDNA) metabarcoding offers a powerful and reproducible solution that can survey across the tree of life with low cost and minimal expertise. Yet there remains a need to condense the complex, multidimensional community information into simple, interpretable metrics of ecological health for environmental management purposes. We developed a taxon-independent community index (TICI) that objectively assigns indicator values to amplicon sequence variants (ASV) rather than taxa, and significantly improves the statistical power and utility of eDNA-based bioassessments. The TICI model training step uses a 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 the average indicator value of the ASVs present at the site. We trained a TICI model on an eDNA dataset from 53 riverine monitoring sites across New Zealand, each sampled with a high level of biological replication (n = 16). Eight short-amplicon metabarcoding assays generated data from a large 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-free 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.