eDNAjoint

#' @srrstats {BS1.2a} README with high-level overview and examples of specifying
#'   prior distributions here

The package eDNAjoint is useful for interpreting observations from 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

You can install the development version of eDNAjoint:

library(devtools)
install_github('abigailkeller/eDNAjoint')

Example

The main functionality in eDNAjoint is the use of jointModel() that will fit the model to data. Further functions like jointSummarize() and detectionCalculate() 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(gobyData)
# run the joint model with two covariates
goby.fit <- jointModel(data = gobyData, cov=c('Filter_time','Salinity'), 
                       family = 'poisson', p10priors = 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
jointSummarize(goby.fit$model, par = 'p10')
#>      mean se_mean    sd  2.5% 97.5%    n_eff Rhat
#> p10 0.003       0 0.001 0.001 0.007 16392.28    1

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.
detectionCalculate(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.

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