A package for estimating population densities and relevant parameters using Random Encounter Models (REMs).

Random Encounter Models allow the estimation of animal population densities and other ecological measures from camera trap data, for a given survey area. The REM can be applied equally to species which exhibit individually identifiable markings and those which do not. The method has been found to perform well when compared to other density estimaton techniques such as Distance sampling and, due to the nature of the data collection medium (i.e. camera traps) may be less demanding in terms of survey effort and allow for greater spatio-temporal analyses of population dynamics.

Here, we aim to produce a comprehensive toolkit for REM analyses in R.

We encourage anyone interested in contributing to get in touch. Contact ar.caravaggi at Gmail.

```
devtools::install_github("timcdlucas/RandEM")
```

Essentials

- Basic REMs
- Effective Detection Distance (EDD; TH)
- iREM (NJ)
- gREM (TL)

Possible additions

- Survey design tools (e.g. draw survey area in Google maps; generate X random points)
- Web app to calculate densities via REM. Bare bones one already here. Launching it from the R package itself would be useful though I currently have no idea how to include a shiny app with a package.
- REST model? Nakashima Y, Fukasawa K, Samejima H. Estimating animal density without individual recognition using information derivable exclusively from camera traps. J Appl Ecol. 2018;55:735–744. https://doi.org/10.1111/1365-2664.13059
- Functions for calculating detection distance and radius for acoustic data.

Contributors are welcome to fork the package and suggest additions or improvements. If you would like to be included as a contributor, please let us know.

Please report it to the issue tracker. Please provide specific details, allowing the error to be reproduced and investigated. Always note the version of R you are using, along with any other relevant software (e.g. RStudio).

This package is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License, version 3, as published by the Free Software Foundation.

This program is distributed in the hope that it will be useful, but without any warranty; without even the implied warranty of merchantability or fitness for a particular purpose. See the GNU General Public License for more details.

A copy of the GNU General Public License, version 3, is available at https://www.r-project.org/Licenses/GPL-

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

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