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

In Rdistance: Density and Abundance from Distance-Sampling Surveys

Rdistance - Distance Sampling Analyses for Abundance Estimation

Description

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 distance function covariates (dfuncEstim()).

• Automatic bootstrap confidence intervals (abundEstim()).

• Parallel processing of bootstrap iterations (parallel argument of abundEstim()).

• Availability of both study-area and site-level abundance estimates (help("predict.dfunc")).

• Rigorous physical measurement requirements and automated conversion when necessary (%#%, setUnits()).

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

• Mixture distance functions for non-standard shapes and thresholds (oneStep.like(), triangle.like(), and huber.like()).

• Automated distance function fitting and selection autoDistSamp().

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

Background:

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 target sightings, either of individuals or groups, and off-transect distances to the original location of the target. When targets are sighted in groups, data include the number of individuals in the group.

Point transect surveys are similar except that observers stop one or more times along the transect to observe targets. Point transects are popular avian survey methods where detections are often auditory cues. Point transects are also for studies using automated auditory detectors or trail cameras. Point transect data consists of radial distances from the observer to the target.

The defining feature of distance sampling is the tendency for probability of detection to decline as off-transect or radial distances increase. Targets far from the observer are generally harder to detect than those closer. In most line transect studies, targets on the transect (off-transect distance = 0) are assumed to be sighted with 100% probability. This assumption allows researchers to estimate the proportion of missed targets and in turn adjust the number of sighted targets for missed detections. Some studies utilize two observers searching the same areas and are able to estimate the proportion of individuals missed on the transect line and thereby eliminate the assumption that all individuals on the line have been observed.

Purpose:

The author's aims are to provide an easy-to-use, rigorous, and flexible analysis option in R for distance-sampling data. The authors believe that beginning users need easy-to-use and easy-to-understand software, while advanced users require greater flexibility and customization, and their aim is to meet the demands of both groups.

Data sets:

Rdistance contains the following example data sets:

• Line-transect sampling of Brewers sparrows in central Wyoming (sparrowDf()).

• Point-transect sampling of Sage Thrashers in central Wyoming (thrasherDf()).

References

Buckland, S.T., Anderson, D.R., Burnham, K.P. and Laake, J.L. 1993. Distance Sampling: Estimating Abundance of Biological Populations. Chapman and Hall, London.

Author(s)

Main author and maintainer: Trent McDonald [e-mail]

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

See Also

Useful links:

• https://mcdonalddatasciences.com/Rdistance.html

• Report bugs at https://github.com/tmcd82070/Rdistance/issues

Rdistance documentation built on May 14, 2026, 5:09 p.m.