Bias in occupancy estimate for a static model

Here we provide some R code to calculate bias in occupancy estimate as a function of the detection probability given various levels of occupancy probability, various number of sites and surveys. Check out the interactive app created using flexdashboard here.

Load package unmarked to carry out occupancy analyses:

library(unmarked)

Load suite of packages tidyverse for data manipulation and visualisation:

library(tidyverse)

Define function to carry out simulations:

occu_par <- function(
  nb_sites = 50, # number of sites
  nb_surveys = 5, # number of surveys
  occpr = 0.3, # occupancy prob 
  detpr = 0.5, # detection prob
  n_sim = 500){ # number of simulations

# preallocate memory for storing occupancy estimates
res <- rep(NA, n_sim)

# simulate data from a static occupancy model n_sim times
for (j in 1:n_sim){
  
  # define state process
  z <- rbinom(nb_sites, 1, occpr) # occupancy state
  
  # pre-allocate memory for matrix of detection/non-detections
  y <- matrix(NA, nrow = nb_sites, ncol = nb_surveys) # detection histories
  
  # define observation process
  for(i in 1:nb_sites){
      prob <- z[i] * detpr
      y[i,1:nb_surveys] <- rbinom(nb_surveys, 1, prob)
  }
  
  # format data
  dat <- unmarkedFrameOccu(y)
  
  # fit static occupancy model w/ constant parameters
  fm <- occu(~ 1 ~ 1, dat)
  
  # get estimate of occupancy prob
  res[j] <- backTransform(fm, type = 'state')@estimate
  
}

# return relative bias in %
bias <- round(mean((res - occpr)/occpr) * 100,1)
return(bias)
}

Grid on detection, occupancy, number of surveys and number of sites:

det <- seq(0.05, 0.95, by = 0.1)
occ <- seq(0.05, 0.95, by = 0.1)
nsites <- c(20, 50, 100)
nsurveys <- c(5, 10, 20)

Define number of simulations per scenario and set the seed for reproducibility:

nsim <- 100
set.seed(666)

Initialize a table for storing results:

sim <- tibble(det = double(), 
              occ = double(),
              nsites = double(),
              nsurveys = double(),
              bias = double())

Loop within loop within loop...

for (i in det){
  for (j in occ){
    for (k in nsites){
      for (l in nsurveys){
        res <- occu_par(nb_sites = k, 
                        nb_surveys = l, 
                        occpr = j, 
                        detpr = i,
                        n_sim = nsim)
        sim <- sim %>% add_row(det = i,
                               occ = j,
                               nsites = k,
                               nsurveys = l,
                               bias = res)
      }
    }
  }
}
sim

## # A tibble: 900 x 5
##      det   occ nsites nsurveys  bias
##    <dbl> <dbl>  <dbl>    <dbl> <dbl>
##  1  0.05  0.05     20        5  298 
##  2  0.05  0.05     20       10  425.
##  3  0.05  0.05     20       20  581.
##  4  0.05  0.05     50        5  640.
##  5  0.05  0.05     50       10  847.
##  6  0.05  0.05     50       20  739.
##  7  0.05  0.05    100        5  914.
##  8  0.05  0.05    100       10  882.
##  9  0.05  0.05    100       20  692.
## 10  0.05  0.15     20        5  213.
## # … with 890 more rows

Visualize bias:

sim %>%
  mutate(nsites = as_factor(nsites),
         nsites = recode(nsites, 
                         '20' = '20 sites',
                         '50' = '50 sites',
                         '100' = '100 sites'),
         nsurveys = as_factor(nsurveys),
         nsurveys = recode(nsurveys,
                           '5' = '5 surveys',
                           '10' = '10 surveys',
                           '20' = '20 surveys')) %>%
  ggplot() +
  aes(x = det,
      y = occ, 
      fill = bias) +
  geom_tile() +
  scale_fill_distiller(palette = "YlGnBu", 
                       direction = 1,
                       name = 'relative bias (%)') +
  facet_wrap(~ nsites + nsurveys, 
             labeller = label_wrap_gen(multi_line = FALSE), 
             ncol = 3) + 
  labs(x = 'detection probability',
       y = 'occupancy probability',
       title = 'What is the amount of occupancy bias in single-season occupancy model ? ',
       subtitle = 'Simulations based on 100 samples per scenario, model fitting accomplished with unmarked') + 
  theme_bw(base_size = 10)

oliviergimenez / bias_occupancy

Bias in occupancy estimate for a static model

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