eDNAjoint

The package eDNAjoint is useful for interpreting observations from paired or semi-paired environmental DNA (eDNA) and traditional surveys. The package runs a Bayesian model that integrates these two data streams to jointly estimate parameters like the false positive probability of eDNA detection and expected catch rate at a site. Optional model variations allow inclusion of site-level covariates that scale the sensitivity of eDNA sampling relative to traditional sampling, as well as estimation of catchability coefficients when multiple traditional gear types are used. Additional functions in the package facilitate interpretation of model fits.

Installation

The most stable version of eDNAjoint can be found on Cran:

install.packages("eDNAjoint")

You can also install the development version of eDNAjoint from ROpenSci:

install.packages("eDNAjoint", repos = "https://ropensci.r-universe.dev")

Example

The main functionality in eDNAjoint is the use of joint_model() that will fit the model to data. Further functions like joint_summarize() and detection_calculate() can be used to help with model fit interpretation.

This example fits the joint model to data from paired, replicated eDNA qPCR and seine sampling observations of endangered tidewater gobies (Eucyclogobius newberryi) from a study by Schmelzle and Kinziger (2016). The following variation of the joint model includes site-level covariates that scale the sensitivity of eDNA sampling relative to traditional sampling.

library(eDNAjoint)
data(goby_data)
# run the joint model with two covariates
goby_fit <- joint_model(data = goby_data, cov = c("Filter_time", "Salinity"), 
                        family = "poisson", p10_priors = c(1, 20), q = FALSE)

And then this model fit can be accessed to do things like summarize the posterior distribution for the probability of a false positive detection, $p_{10}$:

# summarize p10 posterior
joint_summarize(goby_fit$model, par = "p10")
#>      mean se_mean    sd  2.5% 97.5%  n_eff  Rhat
#> p10 0.006   0.002 0.012 0.001  0.06 34.951 1.088

Or to find the number of eDNA samples and traditional survey samples necessary to detect presence of the species at a given expected catch rate:

# find the number of samples necessary to detect presence with 0.9 probability 
# at the mean covariate values, if the expected catch rate (mu) is 0.1, 0.5, or 
# 1 individuals/traditional survey unit.
detection_calculate(goby_fit$model, mu = c(0.1, 0.5, 1), 
                    cov_val = c(0, 0), probability = 0.9)
#>       mu n_traditional n_eDNA
#> [1,] 0.1            24     14
#> [2,] 0.5             5      4
#> [3,] 1.0             3      2

Vignette

You can find much more detailed examples of the functions in eDNAjoint and the model underlying the package in the package vignette.

Code of Conduct

Please note that eDNAjoint is released with a Contributor Code of Conduct. By contributing to this project you agree to abide by its terms.

References

Keller, A.G., Grason, E.W., McDonald, P.S., Ramon-Laca, A., Kelly, R.P. (2022). Tracking an invasion front with environmental DNA. Ecological Applications. 32(4): e2561. https://doi.org/10.1002/eap.2561

Schmelzle, M.C. and Kinziger, A.P. (2016). Using occupancy modelling to compare environmental DNA to traditional field methods for regional-scale monitoring of an endangered aquatic species. Molecular Ecology Resources. 16(4): 895-908. https://doi.org/10.1111/1755-0998.12501