The artemis package for environmental DNA analysis in R. Issue 3 (1st February 2022)

• Record Type: Journal Article
• Authors: Espe, Matthew B.; Johnston, Myfanwy; Blankenship, Scott M.; Dean, Cheryl A.; Bowen, Mark D.; Schultz, Andrew; Schumer, Gregg
• Is Part Of: Environmental DNA. Volume 4:Issue 3(2022)
• Page Start: 523
• Page End: 532
• Publication Date: 2022-02-01
• Subjects: biometry -- data analysis -- environmental DNA -- linear models -- models (statistical) -- polymerase chain reaction -- real‐time; DNA -- Periodicals; Biology -- Periodicals; Microbial ecology -- Periodicals; Biology; DNA; Microbial ecology; Electronic journals; Periodicals; 572.86
• DOI: 10.1002/edn3.277
• Languages: English
• ISSNs: 2637-4943

Abstract

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. 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.