Midwest Fish & Wildlife Conference 2019

AUTHORS: Christopher M. Merkes, US Geological Survey; Katy E. Klymus, US Geological Survey; Richard F. Lance, US Army Corps of Engineers; Emy Monroe, US Fish & Wildlife Service; Catherine A. Richter, US Geological Survey; Caren S. Goldberg, Washington State University; Antoinette J. Piaggio, USDA Animal and Plant Health Inspection Service; Chris C. Wilson, Ontario Ministry of Natural Resources; Joel P. Stokdyk, Margaret E. Hunter, Nathan L. Thompson, Craig A. Jackson, Jon J. Amberg – US Geological Survey

ABSTRACT: Many advances have been made over the last ten years in the field of environmental DNA, and new assays exist for a wide range of target species of interest.  As this technology matures, it is necessary to make methods more standardized to allow better comparisons across studies and enable meta-analysis of species distributions.  One key aspect where this may be possible is with assay sensitivity.  Sensitivity is a critical measure especially when comparing data from multiple markers, and it can be easily described in two measurements: Limit of Detection (LoD; the lowest concentration that can reliably be detected) and Limit of Quantification (LoQ; the lowest concentration that can reliably be quantified).  To facilitate this, an R script has been developed to allow a person with minimal R-coding ability to easily and reliably analyze their data to determine LoD and LoQ of their assays as well as automatically generate plots of their data that puts the values into context for easy understanding.  Putting eDNA assay results into a standardized framework and enabling end users to understand the results more clearly will enhance the value of eDNA data and facilitate its wider application.