R packages

Distance - Distance Sampling Detection Function and Abundance Estimation

A simple way of fitting detection functions to distance sampling data for both line and point transects. Adjustment term selection, left and right truncation as well as monotonicity constraints and binning are supported. Abundance and density estimates can also be calculated (via a Horvitz-Thompson-like estimator) if survey area information is provided. See Miller et al. (2019) for more information on methods and the Articles section of the Distance package website for example analyses.

mrds - Mark-Recapture Distance Sampling

Animal abundance estimation via conventional, multiple covariate and mark-recapture distance sampling (CDS/MCDS/MRDS). Detection function fitting is performed via maximum likelihood. Also included are diagnostics and plotting for fitted detection functions. Abundance estimation is via a Horvitz-Thompson-like estimator. Originally described in Laake & Borchers (2004)

dssd - Distance Sampling Survey Design

Creates survey designs for distance sampling surveys. These designs can be assessed for various effort and coverage statistics. Once the user is satisfied with the design characteristics they can generate a set of transects to use in their distance sampling survey. Many of the designs implemented in this R package were first made available in our ‘Distance’ for Windows software and are detailed in Chapter 7 of Advanced Distance Sampling, Buckland et al. (2004). Package cited as Marshall (2023b).

dsims - Distance Sampling Simulations

Performs distance sampling simulations. ‘dsims’ repeatedly generates instances of a user defined population within a given survey region. It generates surveys design and simulates the detection process, producing data analysed to produce abundance estimates. This process allows users to optimise survey designs for their specific set of survey conditions. The effects of uncertainty in population distribution or parameters can be investigated under various scenarios. All aspects of a survey and analysis can be examined prior to going into the field. Designs available from ‘dssd’ are detailed in Buckland et al. (2004). General distance sampling methods are detailed in Buckland et al. (2001). Package cited as Marshall (2023a).

dsm - Density surface models

This package fits density surface models to spatially-referenced distance sampling data, based on methods developed in Hedley & Buckland (2004) and Miller et al. (2013). Count data are corrected using detection function models fitted using mrds or Distance. Spatial models are constructed using generalized additive models.

Wondering how to get started with the distance sampling R packages? We recommend Miller et al. (2019) as well as case studies for getting started with Distance analyses.

Other packages (no longer under active development)

  • mads
    • Multi-Analysis Distance Sampling: incorporating unidentified sightings, covariate uncertainty and model uncertainty into distance sampling analyses
  • readdst
    • read Distance for Windows data into R: import data from Distance for Windows “projects” and convert them into analyses one can run in the R package mrds.
  • [dsmextra] (https://github.com/densitymodelling/dsmextra)

References

Bouchet, P. J., Miller, D. L., Roberts, J. J., Mannocci, L., Harris, C. M., & Thomas, L. (2020). Dsmextra: Extrapolation assessment tools for density surface models. Methods in Ecology and Evolution, 11(11), 1464–1469. https://doi.org/https://doi.org/10.1111/2041-210X.13469
Buckland, S. T., Anderson, D. R., Burnham, K. P., Laake, J. L., Borchers, D. L., & Thomas, L. (2001). Introduction to distance sampling: Estimating abundance of biological populations. Oxford University Press.
Buckland, S. T., Anderson, D. R., Burnham, K. P., Laake, J. L., Borchers, D. L., & Thomas, L. (2004). Advanced distance sampling. Oxford University Press.
Hedley, S. L., & Buckland, S. T. (2004). Spatial models for line transect sampling. Journal of Agricultural, Biological, and Environmental Statistics, 9(2), 181–199. https://doi.org/10.1198/1085711043578
Laake, J. L., & Borchers, D. L. (2004). Methods for incomplete detection at distance zero. In Advanced distance sampling (pp. 108–189). Oxford University Press.
Marshall, L. (2023a). Dsims: Distance sampling simulations. Retrieved from https://CRAN.R-project.org/package=dsims
Marshall, L. (2023b). Dssd: Distance sampling survey design. Retrieved from https://CRAN.R-project.org/package=dssd
Miller, D. L., Burt, M. L., Rexstad, E. A., & Thomas, L. (2013). Spatial models for distance sampling data: Recent developments and future directions. Methods in Ecology and Evolution, 4(11), 1001–1010. https://doi.org/10.1111/2041-210X.12105
Miller, D. L., Rexstad, E., Thomas, L., Marshall, L., & Laake, J. L. (2019). Distance sampling in R. Journal of Statistical Software, 89(1), 1–28. https://doi.org/10.18637/jss.v089.i01