Abstract
Estimating the abundance and spatial distribution of animal and plant populations is essential for conservation and management. We introduce the R package Distance that implements distance sampling methods to estimate abundance. We describe how users can obtain estimates of abundance (and density) using the package as well as documenting the links it provides with other more specialized R packages. We also demonstrate how Distance provides a migration pathway from previous software, thereby allowing us to deliver cutting-edge methods to the users more quickly.
Highlights
Distance sampling (Buckland, Anderson, Burnham, Borchers, and Thomas 2001; Buckland, Anderson, Burnham, Laake, Borchers, and Thomas 2004; Buckland, Rexstad, Marques, and Oedekoven 2015) encompasses a suite of methods used to estimate the density and/or abundance of biological populations
The rest of the paper has the following structure: we describe data formatting for Distance; candidate detection function models are described and examples fitted in R
In combination with tools such as knitr and rmarkdown (Allaire, Cheng, Xie, McPherson, Chang, Allen, Wickham, Atkins, and Hyndman 2015), the helper functions in Distance provide a useful set of tools to perform reproducible analyses of wildlife abundance for both managers and ecologists
Summary
Distance sampling (Buckland, Anderson, Burnham, Borchers, and Thomas 2001; Buckland, Anderson, Burnham, Laake, Borchers, and Thomas 2004; Buckland, Rexstad, Marques, and Oedekoven 2015) encompasses a suite of methods used to estimate the density and/or abundance of biological populations. The Windows program Distance (or “DISTANCE”; for clarity “Distance for Windows”) can be used to fit detection functions to distance sampling data It was first released (versions 1.0 - 3.0; principally programmed by Jeff Laake while working at the National Marine Mammal Laboratory) as a console-based application (this in turn was based on earlier software TRANSECT, Burnham, Anderson, and Laake 1980 and algorithms developed in Buckland 1992), before the first graphical interface (Distance for Windows 3.5) was released in November 1998. R provides a huge variety of functionality for data exploration and reproducible research, much more than is possible in Distance for Windows Until now those wishing to use our R packages for straightforward distance sampling analyses would have had to negotiate the package mrds (Laake, Borchers, Thomas, Miller, and Bishop 2015) designed for mark-recapture distance sampling (Burt, Borchers, Jenkins, and Marques 2014), requiring a complex data structure to perform analyses. We demonstrate how to use Distance to fit detection functions, perform model checking and selection, and estimate abundance
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