Environmental DNA methods for ecological monitoring and biodiversity assessment in estuaries

Estuaries and Coasts
By: , and 



Environmental DNA (eDNA) detection methods can complement traditional biomonitoring to yield new ecological insights in aquatic systems. However, the conceptual and methodological frameworks for aquatic eDNA detection and interpretation were developed primarily in freshwater environments and have not been well established for estuaries and marine environments that are by nature dynamic, turbid, and hydrologically complex. Environmental context and species life history are critical for successful application of eDNA methods, and the challenges associated with eDNA detection in estuaries were the subject of a symposium held at the University of California Davis on January 29, 2020 (https://marinescience.ucdavis.edu/engagement/past-events/edna). Here, we elaborate upon topics addressed in the symposium to evaluate eDNA methods in the context of monitoring and biodiversity studies in estuaries. We first provide a concise overview of eDNA science and methods, and then examine the San Francisco Estuary (SFE) as a case study to illustrate how eDNA detection can complement traditional monitoring programs and provide regional guidance on future potential eDNA applications. Additionally, we offer recommendations for enhancing communication between eDNA scientists and natural resource managers, which is essential for integrating eDNA methods into existing monitoring programs. Our intent is to create a resource that is accessible to those outside the field of eDNA, especially managers, without oversimplifying the challenges or advantages of these methods.

Publication type Article
Publication Subtype Journal Article
Title Environmental DNA methods for ecological monitoring and biodiversity assessment in estuaries
Series title Estuaries and Coasts
DOI 10.1007/s12237-022-01080-y
Volume 45
Year Published 2022
Language English
Publisher Springer
Contributing office(s) Wetland and Aquatic Research Center
Description 22 p.
First page 2254
Last page 2273
Google Analytic Metrics Metrics page
Additional publication details