Year: 2022
Type: article
Abstract: We introduce artemis, a new package for the R statistical programming environment designed to analyze quantitative polymerase chain reaction (qPCR) data from environmental DNA studies. The artemis package directly addresses several challenges for analyzing eDNA survey data, including the censorship of data generated by qPCR analysis. The performance of artemis models is compared with commonly used regression approaches using both simulated and experimental data. We demonstrate that the models in artemis have several favorable characteristics compared with standard regression approaches, yielding more accurate estimates, less bias, and better prediction performance while offering similar ease of use. Lastly, we discuss trade‐offs and considerations for choosing the most appropriate analysis approach for eDNA survey data.
Source: Environmental DNA
DOI: 10.1002/edn3.277
Authors: Matthew B. Espe, Myfanwy Johnston, Scott M. Blankenship, Cheryl A. Dean, Mark D. Bowen, Andrew Schultz, Gregg Schumer
Language: English
Subfield: Ecology
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,500
Funder: Bureau of Reclamation
Award: G553761351
OpenAlex ID: W4210697847