Improving the reliability of eDNA data interpretation

• Published in: Molecular ecology resources Vol. 21; no. 5; pp. 1422 - 1433
• Main Authors: Burian, Alfred, Mauvisseau, Quentin, Bulling, Mark, Domisch, Sami, Qian, Song, Sweet, Michael
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 01.07.2021
• Subjects: barcoding; Bayesian analysis; Biodiversity; Collating; Conservation; data fusion; Data interpretation; Data processing; Decision making; detection probability; eDNA; Endangered & extinct species; Environmental DNA; false positives; Geographical distribution; metabarcoding; Occupancy; occupancy modelling; Polls & surveys; Reliability analysis; sources of error; Spatial data; species distribution modelling; Target detection; Threatened species; data fusion; sources of error; metabarcoding; barcoding; detection probability; occupancy modelling; eDNA; Bayesian analysis; species distribution modelling; false positives
• ISSN: 1755-098X; 1755-0998
• DOI: 10.1111/1755-0998.13367

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