eDNAjoint (version 0.3.3)

detection_calculate: Calculate the survey effort necessary to detect species presence, given the species expected catch rate.

Description

This function calculates the number of survey effort units to necessary detect species presence using median estimated parameter values from joint_model(). Detecting species presence is defined as producing at least one true positive eDNA detection or catching at least one individual. See more examples in the Package Vignette.

Usage

detection_calculate(
  model_fit,
  mu,
  cov_val = NULL,
  probability = 0.9,
  pcr_n = 3
)

Value

A summary table of survey efforts necessary to detect species presence, given mu, for each survey type.

Arguments

model_fit

An object of class stanfit.

mu

A numeric vector of species densities/capture rates. If multiple traditional gear types are represented in the model, mu is the catch rate of gear type 1.

cov_val

A numeric vector indicating the values of site-level covariates to use for prediction. Default is NULL.

probability

A numeric value indicating the probability of detecting presence. The default is 0.9.

pcr_n

An integer indicating the number of PCR replicates per eDNA sample. The default is 3.

Examples

# \donttest{
# Ex. 1: Calculating necessary effort for detection with site-level
# covariates

# Load data
data(goby_data)

# Fit a model including 'Filter_time' and 'Salinity' site-level covariates
fit_cov <- joint_model(data = goby_data, cov = c('Filter_time','Salinity'),
                       family = "poisson", p10_priors = c(1,20), q = FALSE,
                       multicore = FALSE)

# Calculate at the mean covariate values
# (covariates are standardized, so mean = 0)
detection_calculate(fit_cov$model, mu = seq(from = 0.1, to = 1, by = 0.1),
                    cov_val = c(0,0), pcr_n = 3)

# Calculate mu_critical at salinity 0.5 z-scores greater than the mean
detection_calculate(fit_cov$model, mu = seq(from = 0.1, to = 1, by = 0.1),
                    cov_val = c(0,0.5), pcr_n = 3)

# Ex. 2: Calculating necessary effort for detection with multiple traditional
# gear types

# Load data
data(green_crab_data)

# Fit a model with no site-level covariates
fit_q <- joint_model(data = green_crab_data, cov = NULL, family = "negbin",
                     p10_priors = c(1,20), q = TRUE, multicore = FALSE)

# Calculate
detection_calculate(fit_q$model, mu = seq(from = 0.1, to = 1, by = 0.1),
                    cov_val = NULL, pcr_n = 3)

# Change probability of detecting presence to 0.95
detection_calculate(fit_q$model, mu = 0.1, cov_val = NULL,
                    probability = 0.95, pcr_n = 3)
# }