Environmental DNA (eDNA) has matured into an operational monitoring approach capable of delivering whole-ecosystem insights, spanning microbes to megafauna. For marine park science and management, the key challenge is no longer data generation, but translating complex eDNA signals into robust, trusted metrics that support good decision-making.
As a case study demonstrating this approach, a learning algorithm was applied to a comprehensive eDNA dataset from New Zealand. A taxonomy-independent community index (TICI) was trained that could predict site condition strongly correlated with an existing morphology-based index (the macroinvertebrate community index or MCI), while enabling far greater scalability and consistency.
Critically, the TICI workflow has moved beyond proof-of-concept to operational deployment. It is now accredited to the ISO 17025 standard, representing the first eDNA metabarcoding workflow globally to achieve this level of quality assurance. It has been adopted by government monitoring agencies, generating over 30,000 site condition assessments to date.
This example highlights a clear pathway for overcoming key barriers to eDNA adoption: standardisation, reproducibility, and interpretability. By converting high-dimensional sequence data into defensible, management-relevant metrics, eDNA can support prioritisation, track ecosystem change, and evaluate interventions across marine and coastal systems.
As marine park managers face increasing pressure to monitor biodiversity at scale, approaches that integrate advanced analytics with rigorous quality frameworks will be essential to unlock the full value of eDNA for decision-making.