Improving the reliability of eDNA data interpretation

Global declines in biodiversity highlight the need to effectively monitor density and distribution of threatened species. In recent years, molecular survey methods detecting DNA released by target-species into their environment (eDNA) have been rapidly on rise. Despite providing new, cost-effective tools for conservation, eDNA-based are prone errors. Best field laboratory practices can mitigate some, but risks errors cannot be eliminated accounted for. Here, we synthesize advances data processing that increase reliability interpretations drawn from eDNA data. We review occupancy models consider spatial data-structures simultaneously assess rates false positive negative results. Further, introduce process-based integration metabarcoding as complementing approaches assessments. These will most effective when capitalizing multi-source sets collating with classical citizen-science approaches, paving way more robust decision-making processes conservation planning.