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

Global biodiversity is declining at an ever-increasing rate. Yet effective policies to mitigate or reverse these declines require ecosystem condition data that are rarely available. Morphology-based bioassessment methods are difficult to scale, limited in scope, suffer prohibitive costs, require skilled taxonomists, and can be applied inconsistently between practitioners. Environmental DNA (eDNA) metabarcoding offers a powerful, reproducible and scalable solution that can survey across the tree-of-life with relatively low cost and minimal expertise for sample collection. However, 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 riverine taxon-independent community index (TICI) that objectively assigns indicator values to amplicon sequence variants (ASVs), and significantly improves 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. We trained a TICI model 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; R ²  = 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.
Competing Interests: Shaun P. Wilkinson, Amy A. Gault and Susan A. Welsh are current employees of Wilderlab NZ Ltd., a commercial eDNA processing laboratory. Megan Shaffer was employed by Wilderlab NZ Ltd. during the course of this study. Joshua P. Smith is an employee of Waikato Regional Council, Hamilton, New Zealand. Bruno O. David was employed by Waikato Regional Council, Hamilton, New Zealand, during the course of this study. Andy S. Hicks is currently employed by the Ministry for the Environment, Wellington, New Zealand, and was employed by Hawke’s Bay Regional Council, Napier, New Zealand, during the course of this study. Daniel R. Fake was employed by Hawke’s Bay Regional Council, Napier, New Zealand, during the course of this study. Alastair M Suren is employed by Bay of Plenty Regional Council, Whakatāne, New Zealand.
(©2024 Wilkinson et al.)