# rem: Random Encounter Model function In timcdlucas/RandEM: Population density estimates via Random Encounter Models in R

## Description

This function allows the automated calculation of animal densities.km2 using camera trap data and Random Encounter Modelling.

## Usage

 `1` ```rem(dat, tm, v) ```

## Arguments

 `dat` The data frame `tm` (numeric) The total number of hours all cameras were left in-situ at a focal site `v` (numeric) The distance travelled by the focal species in 24 hours, in kilometres

## Details

The function assumes that the first 4 columns of the dataset contain: 1) An identifying number for each survey location (e.g. 1, 2, 3) 2) The number of individuals of the focal species observed in each capture 3) The radial distance to the detected animal in each capture, given in metres 4) The angle of detection based on the location of the detected animal in each capture, given in radians

For a detailed example of how to use rem, click (here).

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31``` ```data(hDat) ## Split the data by survey site: grpDat <- split_dat(hDat) ## Define tm and v and pass the values to the function: tm <- 3600 v <- 1.4 ## Use the rem function to calculate density estimates. For one survey site: rem(hDat, tm, v) ## Or rem(hDat, tm = 3360, v = 1.4) ## For multiple survey sites, assuming tm and v are constant: rem(dat = grpDat[[1]], tm, v) ## If tm and v differ for each survey site, we can specify them alongside the REM function, # as below. Note that if the focal species is a constant, v should not change. rem(dat = grpDat[[1]], tm = 3600, v = 1.4) rem(dat = grpDat[[2]], tm = 3360, v = 1.4) ## Before calculating variance, define the number of bootstrap iterations: nboots <- 1000 ## Use the bootstrapping function boot_sd on each group dataframe n # (i.e.nboots) times and calculate the standard deviation: remsD <- lapply(grpDat, boot_sd, tm, v, nboots) remsSD <- lapply(remsD, sd) remsSD ```

timcdlucas/RandEM documentation built on May 29, 2019, 3:07 a.m.