Rdistance-package: Rdistance - Distance Sampling Analyses for Abundance...

Rdistance-packageR Documentation

Rdistance - Distance Sampling Analyses for Abundance Estimation


Rdistance contains functions and associated routines to analyze distance-sampling data collected on point or line transects. Some of Rdistance's features include:

  • Accommodation of both point and line transect analyses in one routine (dfuncEstim).

  • Regression-like formula for inclusion of covariate in distance functions (dfuncEstim).

  • Automatic bootstrap confidence intervals (abundEstim).

  • Availability of both study-area and site-level abundance estimates (abundEstim).

  • Classical, parametric distance functions (halfnorm.like, hazrate.like), and expansion functions (cosine.expansion, hermite.expansion, simple.expansion).

  • Non-classic distance functions (Gamma.like, negexp.like, uniform.like) and a non-parametric smoother dfuncSmu).

  • User defined distance functions.

  • Automated distance function fits and selection autoDistSamp.

  • Extended vignettes.

  • print, plot, predict, coef, and summary methods for distance function objects and abundance classes.


Distance-sampling is a popular method for abundance estimation in ecology. Line transect surveys are conducted by traversing randomly placed transects in a study area with the objective of sighting animals and estimating density or abundance. Data collected during line transect surveys consists of sighting records for targets, usually either individuals or groups of individuals. Among the collected data, off-transect distances are recorded or computed from other information (see perpDists). Off-transect distances are the perpendicular distances from the transect to the location of the initial sighting cue. The actual locations of sighted targets are often recorded or computed. When groups are the target, the number of individuals in the group is recorded.

Point transect surveys are similar except that observers stop one or more times along the transect to observe targets. This is a popular method for avian surveys where detections are often auditory cues, but is also appropriate when automated detectors are placed along a route. Point transect surveys collect distances from the observer to the target and are sometimes called radial distances.

A fundamental characteristic of both line and point-based distance sampling analyses is that probability of detecting a target declines as off-transect or radial distances increase. Targets far from the observer are usually harder to detect than closer targets. In most classical line transect studies, targets on the transect (off-transect distance = 0) are assume to be sighted with 100% probability. This assumption allows estimation of the proportion of targets missed during the survey, and thus it is possible to adjust the actual number of sighted targets for the proportion of targets missed. Some studies utilize two observers searching the same areas to estimate the proportion of individuals missed and thereby eliminating the assumption that all individuals on the line have been observed.

Relationship to other software

A detailed comparison of Rdistance to other options for distance sampling analysis (e.g., Program DISTANCE, R package Distance, and R package unmarked) is forthcoming. While some of the functionality in Rdistance is not unique, our aim is to provide an easy-to-use, rigorous, and flexible analysis option for distance-sampling data. We understand that beginning users often need software that is both easy to use and easy to understand, and that advanced users often require greater flexibility and customization. Our aim is to meet the demands of both user groups. Rdistance is under active development, so please contact us with issues, feature requests, etc. through the package's GitHub website (https://github.com/tmcd82070/Rdistance).

Data sets

Rdistance contains four example data sets: two collected using line-transect methods (i.e., sparrowDetectionData and sparrowSiteData) and two collected using point-transect (sometimes called a point count) methods (i.e., thrasherDetectionData and thrasherSiteData).

These datasets demonstrate the type and format of input data required by Rdistance to estimate a detection function and abundance from distance sampling data collected by surveying line transects or point transects. They also allow the user to step through the tutorials described in the package vignettes.

Rdistance requires only detection data to fit detection functions, assuming no covariates in the detection function (see dfuncEstim). Both detection and site data are required to estimate abundance or to include site-level covariates in the detection function (see abundEstim).


The best place to start learning about Rdistance is at the package's GitHub Wiki, which hosts several tutorial vignettes and FAQs (https://github.com/tmcd82070/Rdistance/wiki). Additionally, the examples in the help files for dfuncEstim, abundEstim, and autoDistSamp highlight the package's primary functionality.

A list of routines can be obtained by loading Rdistance and issuing help(package="Rdistance").


Main author and maintainer: Trent McDonald <trent@mcdonalddatasciences.com>

Coauthors: Ryan Nielson, Jason Carlisle, and Aidan McDonald

Contributors: Ben Augustine, James Griswald, Joel Reynolds, Pham Quang, Earl Becker, Aaron Christ, Brook Russelland, Patrick McKann, Lacey Jeroue, Abigail Hoffman, Michael Kleinsasser, and Ried Olson

Rdistance documentation built on July 9, 2023, 6:46 p.m.