The artemis package for environmental DNA analysis in R

Abstract

Given these strengths, sampling for and detecting eDNA using quantitative polymerase chain reaction (qPCR) have gained popularity over the past 20 years (Dejean et al., 2012;Moyer et al., 2014) and are now broadly used to sample and indirectly infer the presence of taxa in a variety of aquatic environments. However, widespread sampling and detection of eDNA by ecologists and conservation biologists are unstandardized, and the field is in need of standards for statistical analysis and reporting (Fediajevaite et al., 2021;Thalinger et al., 2021). In addition, managers need context to understand the biological significance of a set of eDNA observations. This paper describes how probability statements about species presence can be developed using the artemis package and an eDNA sampling scheme. | Estimating [eDNA] via qPCRIn eDNA samples that undergo fluorescence-based quantitative real-time PCR, the amount of eDNA present in the sample is estimated from the number of quantification cycles of qPCR (hereafter the "Cq" value) completed before amplification takes place during qPCR. By this process, the concentration of eDNA is not directly measured. The relationship between eDNA concentration ([eDNA]) and Cq values is determined via a standard curve generated in the laboratory from the assay for the target species. This standard curve formula typically takes the form:

Comparing cost, effort, and performance of environmental DNA sampling and trapping for detecting an elusive freshwater turtle

“…This study was primarily focused on M. temminckii detection, rather than optimizing a published qPCR assay to confidently quantify sample eDNA copy number. Therefore, low qPCR reaction efficiencies, which were generally ignored, may have resulted in non‐detections for samples with low eDNA concentrations typical of this species (Espe et al., 2022; Mauvisseau et al., 2019). Future studies may improve M. temminckii eDNA detection performance by re‐optimizing the qPCR assay for this species.…”

A Hierarchical Model for eDNA Fate and Transport Dynamics Accommodating Low Concentration Samples

“…where y i,j,k is the measured replicate copy number and σ rep is the replicate-level copy number measurement error on the log scale, which could be a function of site, sample, or replicate covariates. Because 1) we condition the quantification process on positive detections and 2) copy number measurements may be censored from below by η q (Espe et al, 2022), the observed replicate-level quantitative data, y obs i,j,k , are:…”

“…Further, eDNA sampling can be a particularly useful tool for aquatic invasive species monitoring, potentially allowing for early detection and eradication (Larson et al, 2020; Morisette et al, 2021; Sepulveda et al, 2020). However, across many applications of eDNA monitoring in aquatic environments, the reliability of ecological inference can be reduced by 1) uncertainty in the source location of detected eDNA (Carraro et al, 2018; Jo and Yamanaka, 2022) and 2) difficulties measuring site and sample eDNA concentrations with minimal bias while accounting for all relevant sources of uncertainty (Ellison et al, 2006; Shelton et al, 2019; Espe et al, 2022). Both of these factors have the potential to reduce the effectiveness of eDNA sampling for management action, depending on their magnitude and how well they are addressed in experimental/monitoring design and statistical analysis.…”

Environmental DNA Methods for Ecological Monitoring and Biodiversity Assessment in Estuaries

“…io/ artem is/ index. html) is one such model, developed and tested in the SFE specifically for eDNA qPCR requirements (Espe et al 2022). The analytical framework accommodates biases such as those that arise from qPCR "censored" data, when the amount of detected eDNA occurs at or below the threshold of detection for an assay (Espe et al 2022).…”

“…html) is one such model, developed and tested in the SFE specifically for eDNA qPCR requirements (Espe et al 2022). The analytical framework accommodates biases such as those that arise from qPCR "censored" data, when the amount of detected eDNA occurs at or below the threshold of detection for an assay (Espe et al 2022). Purposefully designing studies under realistic circumstances is particularly important for management applications of eDNA data and can aid decision making by quantifying uncertainty in survey results.…”